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This work presents the results of a transdisciplinary analysis performed on Harward's Almanac (Dublin, 1666), an extremely rare volume currently housed in the National Library of Ireland. The uniqueness and historical value of the Almanac is related to the presence of nineteen handwritten poems, entered by an anonymous scribe. These record textually important English clandestine satire circulating anonymously in Dublin in the late seventeenth and early eighteenth century. Following a comprehensive historical assessment, it appeared evident that the current order of leaves was incorrect. To reconstruct the correct order of the leaves, and hence the likely sequence in which the manuscript poems were inscribed, this study employed a codicological/paleographic analysis complemented by analytical (X-ray fluorescence, XRF) and statistical (Self Organizing Map, SOM) investigation. Specifically, point XRF analysis was carried out for each handwritten page of the Almanac, allowing identification of ink elemental compositions (iron-based ink) and successfully supporting the validity of historical hypotheses on the poems' order of inscription. The statistical organization of XRF data by SOMs allowed easy bi-dimensional visualization of the data set (54 points) and identification of ink similarities, once more validating the historical assessment. [ABSTRACT FROM AUTHOR]

By the end of 2020, over 16,300 Massive Open Online Courses (MOOCs) from 950 universities worldwide had enrolled over 180 million students. Interest in MOOCs has been matched by significant research on the topic, including a considerable number of reviews. This study uses Machine Learning techniques and human expert supervision to generate a comprehensive systematic literature mapping review that overcomes some limitations of the traditional ones and provides a broader overview of the content and main topics studied in the specialized literature devoted to MOOCs. The sample consisted of 6320 publications automatically classified within six research topics, denominated by human experts: institutional approach, pedagogical approach, evaluation, analytics, participation, and educational resources. The content analysis of the topics identified was conducted using visual network analysis, which supported the identification of different thematic sub-clusters and endorsed the classification. Results from the review show that the lowest production of MOOC papers is within the topics of the pedagogical approach and educational resources. In contrast, participation and evaluation are the most frequent ones. In addition, the most cited papers are on the topics of analytics and resources, being the pedagogical approach and the institutional approach the less cited. This highlights the need for more MOOC research from a pedagogical perspective and calls upon the presence of educators. [ABSTRACT FROM AUTHOR]

Clustering validity indices are designed for evaluating the performance of clustering algorithms in terms of quality of clusters. They are also used for detecting the correct number of clusters. Numerous clustering validity indices have been proposed in the literature for different types of data. However, to the best of our knowledge, there are a few well-known clustering validity indices for graph data. In this paper, we propose a new clustering validity index for graph data called the node-based clustering validity index. The main characteristic of our proposed index is that it captures the exclusive contribution of each node to the separation and compactness of the clusters in a graph. This characteristic gives an advantage to the method which is the detection of the correct number of clusters for cases that two sets of nodes are bonded to each other with a single node. We provide an illustrative example to show that many existing validity indices fail to detect the correct number of clusters in such cases. We evaluate the performance of our proposed index with several experiments on different real-world and synthetic graphs. Experiments show that our proposed node-based index outperforms the existing validity indices for different graph data. [ABSTRACT FROM AUTHOR]

This paper presents a novel clustering approach that utilizes variational autoencoders (VAEs) with disentangled representations, enhancing the efficiency and effectiveness of clustering. Traditional VAE-based clustering models often conflate generative and clustering information, leading to suboptimal clustering performance. To overcome this, our model distinctly separates latent representations into two modules: one for clustering and another for generation. This separation significantly improves clustering performance. Additionally, we employ augmented data to maximize mutual information between cluster assignment variables and the optimized latent variables. This strategy not only enhances clustering effectiveness but also allows the construction of latent variables that synergistically combine clustering information from original data with generative information from augmented data. Through extensive experiments, our model demonstrates superior clustering performance without the need for pre-training, outperforming existing deep generative clustering models. Moreover, it achieves state-of-the-art clustering accuracy on certain datasets, surpassing models that require pre-training. [ABSTRACT FROM AUTHOR]

The k-means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial positions of the cluster centers. The global k-means is a deterministic algorithm proposed to tackle the random initialization problem of k-means but its well-known that requires high computational cost. It partitions the data to K clusters by solving all k-means sub-problems incrementally for all k = 1 , ... , K . For each k cluster problem, the method executes the k-means algorithm N times, where N is the number of datapoints. In this paper, we propose the globalk-means++ clustering algorithm, which is an effective way of acquiring quality clustering solutions akin to those of global k-means with a reduced computational load. This is achieved by exploiting the center selection probability that is effectively used in the k-means++ algorithm. The proposed method has been tested and compared in various benchmark datasets yielding very satisfactory results in terms of clustering quality and execution speed. [ABSTRACT FROM AUTHOR]

As an extension of Fuzzy C-Means (FCM), Evidence C-Means (ECM) is proposed in the framework of Dempster–Shafer theory (DST) and has been applied to many fields. However, the objective function of ECM involves only the distortion between the object and the prototype, which relies heavily on the initial prototype. Therefore, ECM may encounter the problem of local optimization. To solve this problem, this paper introduces ECM with Particle Swarm Optimization (PSO) initialization to determine the initial clustering centroids, and proposes Particle Swarm Optimization-based Evidential C-Means (PSO-ECM), which reduces the influence of bad initial prototypes and improves the local optimality problem of ECM. PSO-ECM is compared with three other clustering algorithms in four experiments and with ECM on a noise-containing dataset. According to the experimental results, PSO-ECM performs well in terms of different clustering validity metrics compared with existing clustering algorithms, has high stability of clustering, and can effectively and stably cluster noise-containing datasets and accurately identify outlier points. [ABSTRACT FROM AUTHOR]

Money laundering identification (MLI) is a challenging task for financial AI research and application due to its massive transaction volume, label sparseness, and label bias. Most of the existing MLI methods focus on individual-level abnormal behavior while neglecting the community factor that money laundering is a collaborative group crime. Furthermore, the massive volume of transactions and the issue of label shifting also impede the application of supervised or semi-supervised models. To this end, this paper proposes an efficient community-oriented algorithm, namely SEGE, to identify money laundering based on structural entropy minimization (SEM) with graph embedding in an unsupervised approach. Experiments on both a private real-world money laundering network and a public synthetic dataset show that our SEGE algorithm derives prominent performance and outperforms the parameterized learning-based graph representation methods. Moreover, we find that there are pervasive sub-communities in the real-world money laundering network. Based on our local algorithm, we propose a real combat strategy against the money laundering group, in which when we have several scattered suspicious accounts in the transaction network, we are able to retrieve the whole money laundering group by the union of sub-communities with both high precision and high recall rates. [ABSTRACT FROM AUTHOR]

Belief and plausibility functions based on evidence theory (ET) have been widely used in managing uncertainty. Various generalizations of ET to fuzzy sets (FSs) have been reported in the literature, but no generalization of ET to q-rung orthopair fuzzy sets (q-ROFSs) has been made yet. Therefore, this paper proposes a novel, simple, and intuitive approach to distance and similarity measures for q-ROFSs based on belief and plausibility functions within the framework of ET. This research addresses a significant research gap by introducing a comprehensive framework for handling uncertainty in q-ROFSs using ET. Furthermore, it acknowledges the limitations inherent in the current state of research, notably the absence of generalizations of ET to q-ROFSs and the challenges in extending belief and plausibility measures to certain aggregation operators and other generalizations including Hesitant fuzzy sets, Bipolar fuzzy sets, Fuzzy soft sets etc. Our contribution lies in the proposal of a novel approach to distance and similarity measures for q-ROFSs under ET, utilizing Orthopairian belief and plausibility intervals (OBPIs). We establish new similarity measures within the generalized ET framework and demonstrate the reasonability of our method through useful numerical examples. Additionally, we construct Orthopairian belief and plausibility GRA (OBP-GRA) for managing daily life complex issues, particularly in multicriteria decision-making scenarios. Numerical simulations and results confirm the usability and practical applicability of our proposed method in the framework of ET. [ABSTRACT FROM AUTHOR]

The purpose of clustering is to partition data similar with each other into a same group and partition data dissimilar with each other into different groups. However, in most existing fuzzy clustering approaches, the membership degrees of an individual belonging to different clusters are relied on the different distances between the individual and different cluster centroids, the similarity between the individual and data in different clusters are ignored, besides, the outliers cannot be distinguished effectively. For improving the clustering performance and addressing the problems in most existing fuzzy clustering approaches, based on the concept that data close to each other should be grouped together and be assigned to a same cluster, in this paper, we present a novel efficient fuzzy clustering approach, in which the adaptive local neighborhood information of each data referring to different clusters is taken into consideration; an entirely new idea that the membership degree values of an individual referring to different clusters should not only depend on the distances between the individual and different cluster centers, but also rely on the distances between the individual and several nearest neighbor data in different clusters is put forward; a new scheme to search for the outliers is presented, a method for identifying the different importance of different features is introduced. Experiments on synthetic and publicly real-world datasets were conducted, the clustering results show that the approach put forward can yield consistently satisfying clustering performance and has significant advantages. [ABSTRACT FROM AUTHOR]

The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results. [ABSTRACT FROM AUTHOR]

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Federated learning has been shown to be efficient for training a global model without needing to collect all data from multiple entities to the centralized server. However, the model performance, communication traffic, and data privacy and security are still the focus of federated learning after it has been developed. In this paper, a composition–decomposition based federated learning, denoted as CD-FL, is proposed. In the CD-FL approach, the global model, composed of K sub-models with the same framework, will be decomposed and broadcast to all clients. Each client will randomly choose a sub-model, update its parameters using its own dataset, and upload this sub-model to the server. All sub-models, including the sub-models before and after updating, will be clustered into K clusters to form the global model of the next round. Experimental results on Fashion-MNIST, CIFAR-10, EMNIST, and Tiny-IMAGENET datasets show the efficiency of the model performance and communication traffic of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the expanding rate of scientific publications, it has become a necessity to summarize scientific documents to allow researchers to keep track of recent developments. In this paper, we formulate the scientific document summarization problem in a multi-view clustering (MVC) framework. Two views of the scientific documents, semantic and syntactic, are considered jointly in MVC framework. To obtain an improved partitioning corresponding to different views, a differential evolution algorithm is utilized as the underlying optimization strategy. After obtaining the final optimal partitioning, various sentence-level features like length, position of the sentences, among others, are investigated to extract high scoring sentences and these extracted sentences are used to form the summary. We have also investigated the use of (a) two embedding spaces to represent the sentences of the documents in semantic space; and (b) use of citation contexts to incorporate their importance in summarizing a given scientific document. Our proposed multi-view based clustering approach is purely unsupervised in nature and, does not utilize any labelled data for the generation of the summary. To show the potentiality of multi-view clustering, a single-view based clustering framework is also developed for the purpose of comparison. The performance of the multi-view based system in comparison to a single-view is first tested on some random articles selected from the recently released SciSummNet 2019 dataset using ROUGE measure. Then, two more datasets namely, CL-SciSumm 2016 and CL-SciSumm 2017 are considered for evaluation. Our investigations prove that the proposed approach yields better results than the existing supervised and unsupervised methods including transformer-based deep learning model, with statistically significant improvements. [ABSTRACT FROM AUTHOR]

One of the significant challenges in the sports industry is identifying the factors influencing match results and their respective weightage. For appropriate recommendations to the team management and the team players, there is a need to predict the match and quantify the important factors for which prediction models need to be developed. The second thing required is identifying talented and emerging players and performing an associative analysis of the important factors to the match-winning outcome. This paper formulates a hybrid machine learning-clustering-associative rules model. This paper also implements the framework for cricket matches, one of the most popular sports globally watched by billions around the world. We predict the match outcome for One day Internationals (ODIs) and Twenty 20 s (T20s) (two formats of Cricket representing fifty over and twenty over versions respectively) adopting state-of-the-art machine learning algorithms, Random Forest, Gradient Boosting, and Deep neural networks. The variable importance is computed using machine-learning techniques and further statistically validated through the regression model. The emerging talented players are identified by clustering. Association rules are generated for determining the best possible winning outcome. The results show that environmental conditions are equally crucial for determining a match result, as are internal quantitative factors. The model is thus helpful for both team management and for players to improve their winning strategy and also for discovering emerging players to form an unbeatable team. [ABSTRACT FROM AUTHOR]

The circular economy and its various recirculation loops have become a major study subject over recent years, particularly in the field of agriculture, which is a significant source of waste production. There have been several studies focused on transforming agricultural lignocellulosic waste with "sustainable" processes: economically viable, socially accepted, and environmentally friendly. Thanks to "life cycle thinking", it is possible to assess these potential environmental impacts. However, these environmental analyses generally require a massive volume of specific data, the collection of which can be time-consuming and tedious, or impossible to practice. On the other hand, scientific articles describing the processes for the valorization of agricultural by-products are intriguing but rarely exploited sources of data. In this paper, a hybridization of data science techniques and environmental analysis was proposed to improve life cycle analysis (LCA) thanks to Machine Learning (ML). ML part of the proposed approach is based on unsupervised learning, which is composed of two methods: dimension reduction using the Multidimensional Scaling and clustering technique using k-means. Composed of five steps and dedicated to researchers or R&D engineers, the approach is oriented towards offering a decision on technologies and processes for waste to energy in the early eco-design step. The case study in the domain of pre-treatment processes for corn stover and rice straw is detailed. The results show that all impacts that concern the chemical pollution of soil and water are found in the same cluster. Other impacts are detected in the same cluster which is related to the land use and the land transformation. In the same vein, two purely mechanical pre-treatments have been identified and grouped by Multidimensional Scaling and k-means. [ABSTRACT FROM AUTHOR]

The fog-assisted cloud computing gives better quality of service (QoS) to Internet of things (IoT) applications. However, the large quantity of data transmitted by the IoT devices results in the overhead of bandwidth and increased delay. Moreover, large amounts of data transmission generate resource management issues and decrease the system's throughput. This paper proposes the optimized task scheduling and preemption (OSCAR) model to overcome the limitations and improve the QoS. The dataset used for the study is a real-time crowd-based dataset which provides task information. The processes involved in this paper are as follows: (i) Initially, the tasks from the IoT devices are clustered based on the priority and deadline by implementing expectation–maximization (EM) clustering to decrease the computational complexity and bandwidth overhead. (ii) The clustered tasks are then scheduled by implementing a modified heap-based optimizer based on the QoS and service level agreement (SLA) constraints. (iii) Distributed resource management is performed by allocating resources to the tasks based on multiple constraints. The categorical deep Q network is the deep reinforcement learning model is implemented for this purpose. The dynamic nature of tasks from the IoT devices is addressed by performing preemption of tasks using the ranking method, where the tasks with higher priority, with a short deadline replaces less priority task by moving it into the waiting queue. The proposed model is experimented with in the iFogsim simulation tool and evaluated in terms of average response time, loss ratio, resource utilization, average makespan time, queuing waiting time, percentage of tasks satisfying the deadline and throughput. The proposed OSCAR model outperforms the existing model in achieving the QoS and SLA with maximal throughput and reduced response time. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) is a paradigm that intends to decouple the control and data layers. It has provided us with a shift in the power of the network to turn into a singular centralized entity. This allows for the introduction of a better form of network management. The traditional SDN architecture uses a single controller to control the networks, which can be prone to exhibiting the flaws of a Single Point of Failure (SPoF) system. In order to overcome the hurdle of SPoF, researchers have come up with practices involving initializing a cluster of controllers to handle failing controllers. However, minimal advances exist on the frontier of controller heterogeneity, and mobility in SDNs. In this paper, we propose ASM-SDN as a solution to minimize the packet loss during Station Migration scenarios in Heterogeneous Controllers Clusters. The proposed solution incorporates three complementary mechanisms to establish Fault Tolerance: Controller Prioritization mechanism based on the Markov Chain rule, the Migrating Station Detection mechanism to support Station Migration in ASM-SDN externally, and Flow Table Modifier mechanisms, for controllers that lack the feature set for Automated Station Migration. The results indicate that ASM-SDN can provide better network performance in terms of mobility in comparison to the traditional system. [ABSTRACT FROM AUTHOR]

The Kmeans clustering algorithm is widely used for the advantages of simplicity and efficient operation. However, the lack of clustering centers in the algorithm usually causes incorrect category of some discrete points. Therefore, in order to obtain more accurate clustering results when studying the factors affecting the professional growth of outstanding teachers, this paper proposes an improved algorithm of Kmeans combined with DBSCAN. Observing the clustering results of the influencing factors and calculating the evaluation standard values of the clustering results, it is found that the optimized DB-Kmeans algorithm has obvious improvements in the accuracy of the clustering results, and the clustering effect of the algorithm on edge points is more advantageous than the original algorithms according to the scatter diagram. [ABSTRACT FROM AUTHOR]

Load balancing is critical for container-based cloud computing environments for several reasons. A lack of appropriate load balancing techniques could result in a decrease in performance and possible service interruptions as some nodes get overloaded, while others are left underutilized. Cloud service providers can reduce latency and boost system performance by strategically placing containers using clustering algorithms. These techniques aid in efficiently using resources and load balancing by clustering related containers together according to their shared attributes. Clustering strategies are effective in allocating and controlling resources to meet the demands of a changing workload. Algorithms for clustering combine related workloads or containers into clusters, improving performance isolation and maximizing resource usage. One popular methodology for data clustering is the K-Medoid Clustering Algorithm. It is especially helpful when working with categorical data or when the dataset contains outliers. K-medoids is an unsupervised clustering approach where the core of the cluster is made up of data points known as “medoids.” A medoid is a location in the cluster whose total distance to every object in the cluster—also known as its dissimilarity—is as small as possible. Any appropriate distance function may be used, such as the Manhattan distance, the Euclidean distance, or another one. Thus, by choosing K medoids from our data sample, the K-medoids method splits the data into K clusters. This work presents the K-Medoid clustering technique for containers, which can enhance load balancing, decrease resource execution times, and increase resource utilization rates all at the same time. The results of the experiment show that the proposed method outperforms MACO and FCFS in terms of throughput by about 70% when number of cloudlets increased. The relative improvement of execution time of the proposed K-medoid algorithm w.r.t FCFS is about 50%. [ABSTRACT FROM AUTHOR]

Design of effective algorithm for reliable and energy optimized secure routing protocol (SRP) for wireless sensor networks (WSNs) is a demanding design issue now. To handle this problem, we propose a trust and encryption-based SRP based on trust modelling with intrusion detection, elliptic curve cryptography (ECC), clustering, fuzzy rules and ant colony optimization (ACO)-oriented SRP for WSN routing. In this paper, an extended convolutional neural networks with Schrodinger equation and particle swarm optimization is proposed for developing and intrusion detection-based trust modelling. Moreover, a new node authentication scheme and an encryption-based secure routing protocol are also proposed in this work for increasing the security. This proposed secure protocol known as trust and ECC encryption-based ACO-SRP (TECC-ACO-SRP) performs authentication and trust analysis on the nodes using intrusion detection, and then, the data are communicated after data encryption using ECC encryption technique. This proposed system combines dominant set clustering with fuzzy rules to make clusters with similar type of nodes as members and then selects cluster heads (CHs) for every cluster. This SRP ensures improved security, reduced delay and energy usage with higher packet delivery ratio than other existing SRPs. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important approaches used to extend the lifetime of Wireless Sensor Networks (WSN). The fundamental metric taken by clustering algorithms is energy enhancement. Moreover, network coverage and load balance are two important approaches that play crucial roles in improving network lifetime and delivery since the former focuses on maximizing the use of all network resources, while the second is based on distributing the load between the nodes to enhance the energy consumption. As the challenge of clustering nodes in an energy-efficient way is an NP-Hard problem, firefly optimization algorithm is used to address this challenge. However, the proposed solutions focus on centralized processing of the algorithm, which makes them unsuitable for large-scale WSN. In this paper, a double firefly based efficient clustering solution is proposed for large-scale WSN which is implemented in a decentralized fashion to improve the lifetime and packet delivery. The first firefly algorithm is used by each node to move to the best initial Cluster Head (CH) by performing a balance of belonging between the clusters, while the second algorithm is used only between the initial CHs to eliminate membership redundancy and optimally construct balanced clusters. The simulation results show that our proposed solution significantly improves the network lifetime as well as the delivery rate. [ABSTRACT FROM AUTHOR]

Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of covering a variety of nodes and edges. The members of these networks usually have metadata whose analysis can lead to the discovery of knowledge. One way to analyze such data is clustered where high-quality clustering requires effective similarity calculation. Most of the existing clustering methods do not pay attention to the use of metadata or the characteristics of network members. On the other hand, they are only able to process small and medium-sized networks due to the amount of memory and execution speed. This paper presents a hybrid approach for heterogeneous network clustering to overcome these problems. The structural similarity in this approach is calculated by the graph embedding method, which we call learning-based. Attribute similarity is calculated by a scoring method that we call similarity-based. In the experimental study, we compared the proposed method with collaborative approaches based on similarity on the real-world networks. The experimental findings demonstrate the superiority of the proposed method in terms of entropy, memory consumption, execution time, and density in certain cases. [ABSTRACT FROM AUTHOR]

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are most used to monitor remote environments. Multitudinous sensor nodes gather data in a self-governing manner, operating on an exhaustive source of energy or battery. Clustering process structures the network into a hierarchy wherein sensor nodes gather data passed to selected cluster head nodes which perform data processing, aggregation, and transfer it to a base station. Prolonging network lifetime and enhancing total data transmission to base station are major challenges in wireless sensor network and same is addressed in this work. A distributed energy-based epoch is used in this paper to determine node eligibility to become cluster head and a multi-parameter-weighted scalarization function is proposed to determine best cluster head candidates in order to manage dynamic and multi-characteristic node heterogeneity. The parameters used are distance to base station, expected cluster head lifetime, average cluster member node lifetime and maximum power consumed by a cluster member node. A novel weight computation strategy using analytical hierarchy process is introduced in this paper which enhances the optimality of scalarization function value. The proposed algorithm is distributed over two phases as network setup phase and clustering phase. The network setup phase computes the energy model and optimal number of cluster heads. The second phase proposes the cluster head selection process using weight-based scalarization and introduces the novel weight selection method. Finally, network operation enters the data transmission phase. The results show an enhancement in throughput at base station, with an increase of close to 30% along with an increase in the network lifetime of up to 20% as measured by last node death. The simulation results are produced in comparison with the considered base protocol of DEEC as well as other protocols using similar concepts for implementation. However, utilization of a two-step cluster heads selection process including unique node epochs for shortlisting and scalarization function-based node fitness, along with optimal weight selection procedure, has led the proposed model to give better results on simulation and analyzation than preexisting algorithms. [ABSTRACT FROM AUTHOR]

Tensor factorization has been widely applied in computer vision and machine learning area. Nonnegative Tucker decomposition (NTD) is a popular tensor factorization technique. However, it neglects the geometrical structure of the data space and the available label information of sample data, lowering the data representation performance. To overcome this defect, in this paper, we propose a novel semi-supervised NTD method, named graph regularized discriminative nonnegative Tucker decomposition (GDNTD), which incorporates the graph construction of the data space and the available label information of the sample data into NTD. Specifically, a graph construction is utilized to preserve the geometric structure between data by a graph regularization term. Then, a label matrix is presented for guiding the data representation by a label constraint regularization term. Finally, based on the NTD property of maintaining the internal structure of data, the graph regularizer and the label regularizer are integrated into NTD to generate the proposed method. Thus, GDNTD can extract the part-based representation, preserve the local geometrical structure of the data space, and improve the discriminative ability of the learned model simultaneously, greatly boosting the model's data representation performance. We test the proposed method through a set of evaluations on four image datasets. Experimental results show that the GDNTD method outperforms state-of-the-art approaches, demonstrating its strong potential for data representation. [ABSTRACT FROM AUTHOR]

With the rapid development of big data and the Internet, cross-modal retrieval has become a popular research topic. Cross-modal hashing is an important research direction in cross-modal retrieval, due to its highly efficiency and small memory consumption. Recently, many unsupervised cross-modal hashing methods achieved great results on cross-modal retrieval tasks. However, how to narrow the heterogeneous gap between different modalities and generate more discriminative hash codes are still the main problems of unsupervised hashing. In this paper, we propose a novel unsupervised cross-modal hashing method Pseudo-label Driven Deep Hashing to solve aforementioned problems. We introduce clustering into our modal to obtain initialized semantical information called pseudo-label, and we propose a novel adjusting method that uses pseudo-labels to adjust joint-semantic similarity matrix. We construct a similarity consistency loss function that focuses on the heterogeneity gap between different modalities, and a real values and binary codes fine-tuning strategy for closing the gap between real value space and Hamming space. We conduct experiments on five datasets including three natural datasets which have larger inter-class distances and two medical datasets which have smaller inter-class distances, the results demonstrate the superiority of our method compared with several unsupervised cross-modal hashing methods. [ABSTRACT FROM AUTHOR]

Model pruning has gained increasing attention in the field of network model compression. Although current methods combine GANs with model pruning, their pruning granularity is insufficient to achieve better compression ratios for models. Moreover, they cannot recognize similar filters, leading to redundancy and an inability to learn the optimal pruning structure. To address these issues, we propose a fine-grained generative adversarial network (GAN) for pruning models in this paper. Specifically, we utilize shape masks to learn the shape properties of the model to achieve larger compression ratios. Additionally, we employ mean-shift clustering to prune similar filters and tackle the redundancy issue they cause. Extensive experiments demonstrate the effectiveness of our method, which we validate on the CIFAR-10/100 and ILSVRC-2012 datasets. The results show that our approach outperforms state-of-the-art methods significantly. [ABSTRACT FROM AUTHOR]

We revisit the problem of fair clustering, first introduced by Chierichetti et al. (Fair clustering through fairlets, 2017), which requires each protected attribute to have approximately equal representation in every cluster, i.e., a Balance property. Existing solutions to fair clustering are either not scalable or do not achieve an optimal trade-off between clustering objectives and fairness. In this paper, we propose a new notion of fairness which we call τ -ratio fairness, that strictly generalizes the Balance property and enables a fine-grained efficiency vs. fairness trade-off. Furthermore, we show that a simple greedy round-robin-based algorithm achieves this trade-off efficiently. Under a more general setting of multi-valued protected attributes, we rigorously analyze the theoretical properties of the proposed algorithm, the Fair Round-Robin Algorithm for Clustering Over-End ( FRAC OE ). We also propose a heuristic algorithm, Fair Round-Robin Algorithm for Clustering (FRAC), that applies round-robin allocation at each iteration of a vanilla clustering algorithm. Our experimental results suggest that both FRAC and FRAC OE outperform all the state-of-the-art algorithms and work exceptionally well even for a large number of clusters. [ABSTRACT FROM AUTHOR]

Radio jamming represents a serious threat in wireless networks that can cause denial of service (DoS) attacks for the network users. The jammer transmits jamming signals to disrupt the normal operation of the wireless networks. With proliferation of unmanned aerial vehicle (UAV)-based wireless networks and Internet of Things technologies, jamming attacks have become a major challenge to the practical adoption of these technologies. In this paper, we investigate the problem of optimizing the performance of the UAV data collection from ground IoT devices in presence of a malicious jammer. The IoT devices are clustered, and the UAV collects the data from the cluster heads (CHs) such that the harmful effect of the jammer is mitigated. The objective is to minimize the flight time of the UAV while collecting the data from the ground IoT devices in presence of the jamming attack. We modeled our problem mathematically as an integer linear program (ILP). Due to the difficulty of solving the optimization problem when the scale of the problem is large, we proposed Jamming-Aware UAV Routing and IoT Devices Clustering Algorithm (JA-RC). This algorithm is Genetic-based and can solve the original optimization problem in a more efficient way. Then, we investigate the performance of the proposed algorithm under different settings and scenarios. [ABSTRACT FROM AUTHOR]

Image clustering plays an important role in computer vision and machine learning. However, most of the existing clustering algorithms flatten the image into one-dimensional vector as an image representation for subsequent learning without fully considering the spatial relationship between pixels, which may lose some useful intrinsic structural information of the matrix data samples and result in high computational complexity. In this paper, we propose a novel two-dimensional k-subspace clustering (2DkSC). By projecting data samples into a discriminant low-dimensional space, 2DkSC maximizes the between-cluster difference and meanwhile minimizes within-cluster distance of matrix data samples in the projected space, thus dimensionality reduction and clustering can be realized simultaneously. The weight between the between-cluster and within-cluster terms is derived from a Bhattacharyya upper bound, which is determined by the involved input data samples. This weighting constant makes the proposed 2DkSC adaptive without setting any parameters, which improves the computational efficiency. Moreover, 2DkSC can be effectively solved by a standard eigenvalue decomposition problem. Experimental results on three different types of image datasets show that 2DkSC achieves the best clustering results in terms of average clustering accuracy and average normalized mutual information, which demonstrates the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) include a collection of distinct and autonomous nodes that move independently and send data using wireless channels. Clustering and secure routing are treated as proficient techniques to achieve energy efficiency and network stability. Different methods have been presented for addressing the issues of mobility and battery limited characteristics of mobile nodes in MANETs. This paper aims to develop new cluster-based secure routing protocols in MANETs. This paper presents a new Type II fuzzy-based clustering with improved ant colony optimization-based routing (T2FCATR) protocol for secured data transmission in MANETs. Initially, a new type II fuzzy-based clustering process takes place to construct the clusters. Then, the routing issue is addressed by ACO with the Tumbling technique, which offers intercluster routing from CHs to BS. In addition, a hybrid data transmission mechanism is also involved to reduce the number of data transmissions and thereby achieve energy efficiency. The performance of the T2FCATR model has been validated against several aspects. The simulation outcome implied that the T2FCATR model achieves minimum clustering overhead, minimum variations in CHs and CMs. As a whole, it can be concluded that the T2FCATR model is superior to all the compared methods under several aspects. [ABSTRACT FROM AUTHOR]

Topology control is a significant research problem when it comes to designing energy-efficient wireless sensor networks (WSNs). It is a metric for evaluating network service quality. To ensure the quality of network services, it is critical to provide energy-efficient sensor node scheduling in order to extend the network's lifespan. In this paper, we propose an energy-aware scheduling protocol (ESP)-based hybrid metaheuristic technique for minimizing the amount of energy consumption and ensuring sufficient coverage for the monitored area while maximizing the network lifespan for WSNs. The ESP consists of two phases: distributed clustering and sensor node scheduling. The clustering phase implements the DBSCAN clustering algorithm with slide modification to cluster the sensor nodes in the area of interest into clusters of nodes, and the efficient periodic cluster head election approach is proposed based on criteria such as remaining energy, number of neighbors, and distance for each node in the cluster. This clustering is only performed at the beginning to group the nodes into clusters. Then, only the cluster head election will be done every period. In the scheduling phase, we model the scheduling optimization problem using three objectives: reducing the number of uncovered zones, minimizing the number of active sensors, and selecting the active sensor nodes with the maximum remaining energy. This scheduling optimization model is solved using a hybrid metaheuristic algorithm. When the genetic algorithm's operators were added to the regular Cuckoo algorithm, the balance between exploration and exploitation abilities was improved even more. The algorithms were able to search a larger area. Extensive simulation experiments were conducted using the OMNeT++ network simulator. The results show that the proposed ESP protocol introduces better performance in terms of coverage ratio, active sensor ratio, energy consumption, network lifespan, and execution time compared to other existing techniques. [ABSTRACT FROM AUTHOR]

Climate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster. [ABSTRACT FROM AUTHOR]

Many practical problems involve the optimization of computationally expensive blackbox functions. The computational cost resulting from expensive function evaluations considerably limits the number of true objective function evaluations allowed in order to find a good solution. In this paper, we propose a clustering-based surrogate-assisted evolutionary algorithm, in which a clustering-based local search technique is embedded into the radial basis function surrogate-assisted evolutionary algorithm framework to obtain sample points which might be close to the local solutions of the actual optimization problem. The algorithm generates sample points cyclically by the clustering-based local search, which takes the cluster centers of the ultimate population obtained by the differential evolution iterations applied to the surrogate model in one cycle as new sample points, and these new sample points are added into the initial population for the differential evolution iterations of the next cycle. In this way the exploration and the exploitation are better balanced during the search process. To verify the effectiveness of the present algorithm, it is compared with four state-of-the-art surrogate-assisted evolutionary algorithms on 24 synthetic test problems and one application problem. Experimental results show that the present algorithm outperforms other algorithms on most synthetic test problems and the application problem. [ABSTRACT FROM AUTHOR]

Impressive performance has been achieved when learning graphs from data in clustering tasks. However, real data often contain considerable noise, which leads to unreliable or inaccurate constructed graphs. In this paper, we propose adaptive data correction-based graph clustering (ADCGC), which can be used to adaptively remove errors and noise from raw data and improve the performance of clustering. The ADCGC method mainly contains three advantages. First, we design the weighted truncated Schatten p-norm (WTSpN) instead of the nuclear norm to recover the low-rank clean data. Second, we choose clean data samples that represent the essential properties of the data as the vertices of the undirected graph, rather than using all the data feature points. Third, we adopt the block-diagonal regularizer to define the edge weights of the graph, which helps to learn an ideal affinity matrix and improve the performance of clustering. In addition, an efficient iterative scheme based on the generalized soft-thresholding operator and alternating minimization is developed to directly solve the nonconvex optimization model. Experimental results show that ADCGC both quantitatively and visually outperforms existing advanced methods. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is a popular approach to extract intrinsic features from the original data. As the nonconvexity of NMF formulation, it always leads to degrade the performance. To alleviate the defect, in this paper, the self-paced regularization is introduced to find a better factorized matrices by sequentially selecteing data in the learning process. Additionally, to find the low-dimensional manifold embeded in the high-dimensional space, adaptive graph is introduced by using dynamic neighbors assignment. An alternating iterative algorithm is designed to sovle the proposed mathematical factorization formulation. The experimental results are given to show the effectiveness of the proposed approach in comparison with state-of-the-art algorithms on six public datasets. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems and their solution algorithms are of great importance as single-objective optimization problems are not usually a true representation of many real-world problems. In general, multi-objective optimization problems result in a large set of Pareto optimal solutions. Each solution in this set is optimal with some trade-offs. Therefore, it is difficult for the decision-maker to select a solution, especially in the absence of subjective or judgmental information. Moreover, an analysis of all the solutions is computationally expensive and, hence, not practical. Thus, researchers have proposed several techniques such as clustering and ranking of Pareto optimal solutions to reduce the number of solutions. The ranking methods are often used to obtain a single solution, which is not a good representation of the entire Pareto set. This paper deviates from the common approach and proposes a novel graph-theoretical clustering method. The quality of the clustering based on the Silhouette score is used to determine the number of clusters. The connectivity in the objective space is used to find representative solutions for clusters. One step forward, we identify 'extreme solutions'. Hence, the reduced set contains both extreme solutions and representative solutions. We demonstrate the performance of the proposed method by using different 3D and 8D benchmark Pareto fronts as well as Pareto fronts from a case study in Royal Australian Navy. Results revealed that the reduced set obtained from the proposed method outperforms that from the K-means clustering, which is the most popular traditional clustering approach in Pareto pruning. [ABSTRACT FROM AUTHOR]

Path planning is an important task for robot motion, unmanned aerial vehicle obstacle avoidance, and smart cars. Sampling-based algorithms have achieved significant success in this task. The rapidly random-exploring tree (RRT) is one of the representative algorithms. However, it is exceedingly difficult to strike a balance between path quality and search efficiency. In this paper, we propose an enhanced RRT* with clustering and presearching (ERCP) algorithm to handle this issue. In the clustering phase, we construct an 8-degree undirected graph according to the neighborhood and obstacles. Then, we adopt the Markov clustering technique, which is appropriate for spatial data. The clustering process is efficient since the coarse-grained map considers only points at integer locations. In the presearching phase, we utilize the clustering results to abstract the intact state space as an undirected graph. We employ Dijkstra's algorithm to perform a presearch on the graph to determine the effective sampling area. In the path planning phase, we use RRT* to extend the space-filling tree within the effective sampling regions. Experiments were conducted on ten maps with different levels of obstacle complexity. The results reveal that ERCP can achieve a more convincing balance between path quality and search efficiency than the three state-of-the-art algorithms with little sacrifice. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel density-grid-based method for clustering k-dimensional data. KIDS, an acronym for K-dimensional Ink Drop Spread, detects densely-connected pieces of data in k-dimensional grids. It enables one to simultaneously exploit the advantages of fuzzy logic, as well as both density-based and grid-based clustering. In the proposed method, the k-dimensional data space is divided into different cells. Input data records are mapped to the cells. The data points are then spread in the k-dimensional cells, just like what happens to ink drops in water. So the cells adjacent to the data cells also represent the data. Eventually, the impacts of all data grid cells are condensed and compared with the threshold to compute the final clusters. The experimental results show that the method has superior quality and efficiency in both low and high dimensions. In addition, the method is not only robust to noise but it is also capable of finding clusters of arbitrary shapes. [ABSTRACT FROM AUTHOR]

The exponential growth in the number of scientific articles has made it difficult for the researchers to keep themselves updated with the new developments. Scientific document summarization solves this problem by providing a summary of essential contributions. In this paper, we have presented a novel method of scientific document summarization using a multi-objective differential evolution technique. Here, firstly distinct important sentences are extracted by using citation contextualization. These sentences are further clustered using the concept of multi-objective clustering. Two objective functions, PBM index, and XB index, measuring the compactness and separation of sentence clusters, are simultaneously optimized utilizing the search capability of multi-objective differential evolution. We have conducted our experiments on CL-SciSumm 2016, CL-SciSumm 2017, CL-SciSumm 2018, and CL-SciSumm 2019 datasets. Obtained results of CL-SciSumm 2016 and CL-SciSumm 2017 are compared with the state-of-the-art methods. Evaluation results demonstrate that our method outperforms others in terms of ROUGE-2, ROUGE-3, and ROUGE-SU4 scores. [ABSTRACT FROM AUTHOR]

Time series are ubiquitous in data mining applications. Similar to other types of data, annotations can be challenging to acquire, thus preventing from training time series classification models. In this context, clustering methods can be an appropriate alternative as they create homogeneous groups allowing a better analysis of the data structure. Time series clustering has been investigated for many years and multiple approaches have already been proposed. Following the advent of deep learning in computer vision, researchers recently started to study the use of deep clustering to cluster time series data. The existing approaches mostly rely on representation learning (imported from computer vision), which consists of learning a representation of the data and performing the clustering task using this new representation. The goal of this paper is to provide a careful study and an experimental comparison of the existing literature on time series representation learning for deep clustering. In this paper, we went beyond the sole comparison of existing approaches and proposed to decompose deep clustering methods into three main components: (1) network architecture, (2) pretext loss, and (3) clustering loss. We evaluated all combinations of these components (totaling 300 different models) with the objective to study their relative influence on the clustering performance. We also experimentally compared the most efficient combinations we identified with existing non-deep clustering methods. Experiments were performed using the largest repository of time series datasets (the UCR/UEA archive) composed of 128 univariate and 30 multivariate datasets. Finally, we proposed an extension of the class activation maps method to the unsupervised case which allows to identify patterns providing highlights on how the network clustered the time series. [ABSTRACT FROM AUTHOR]

Fitness landscape analysis is a tool that can help us gain insight into a problem, determine how hard it is to solve a problem using a given algorithm, choose an algorithm for solving a given problem, or choose good algorithm parameters for solving the problem. In this paper, fitness landscape analysis of hyperheuristics is used for clustering instances of three scheduling problems. After that, good parameters for tree-based genetic programming that can solve a given scheduling problem are calculated automatically for every cluster. Additionally, we introduce tree editing operators which help in the calculation of fitness landscape features in tree based genetic programming. A heuristic is proposed based on introduced operators, and it calculates the distance between any two trees. The results show that the proposed approach can obtain parameters that offer better performance compared to manual parameter selection. [ABSTRACT FROM AUTHOR]

A major challenge in Cloud-Fog settings is the scheduling of workflow applications with time constraints as the environment is highly volatile and dynamic. Furthermore, adding the complexities of handling IoT nodes, as the major owners of the workflow requests, renders the problem space even harder to address. This paper presents a hybrid scheduling-clustering method for addressing this challenge. The proposed lightweight, decentralized, and dynamic clustering algorithm is based on fuzzy inference with intrinsic support for mobility to form stable and well-sized clusters of IoT nodes while avoiding global clustering and recurrent re-clustering. The proposed distributed method uses Cloud resources along with clusters of mobile and inert Fog nodes to schedule time-constrained workflow applications with considering a proper balance between contradicting criteria and promoting scalability and adaptability. The Velociraptor simulator (version 0.6.7) has been used to throughtly examine and compare the proposed method in real workloads with two contemporary and noteworthy methods. The evaluation results show the superiority of the proposed method as the resource utilization is about 20% better and the schedule success rate is almost 21% better compared with the two other methods. Also, other parameters such as throughput and energy consumption have been studied and reported. [ABSTRACT FROM AUTHOR]

Anomalies indicate impending failures in expensive industrial devices. Manufacturers of such devices or Plant Managers depend heavily on anomaly detection algorithms to perform monitoring and predictive maintenance activities. Since false alarms directly impact any industrial manufacturer's revenue, it is crucial to reduce the number of false alarms generated by anomaly detection algorithms. Here in this paper, we have proposed multiple solutions to address this ongoing problem in the industry. The proposed unsupervised solution, Multi-Generations Tree (MGTree) algorithm, not only reduced the false positive alarms but is also equally effective on small and large datasets. MGTree has been applied to multiple industrial datasets such as Yahoo, AWS, GE, and machine sensors for evaluation purposes. Our empirical evaluation shows that MGTree performs favorably to Isolation Forest (iForest), One Class Support Vector Machine (OCSVM), and Elliptic Envelope in terms of correctness of the identification (True-Positive and False-Positive) of the anomalies. We have also proposed a time series prediction algorithm Weighted Time-Window Moving Estimation (WTM), which does not rely on the dataset's stationary characteristics and is evaluated on multiple time-series datasets. The hybrid combination of WTM and MGTree, Uni-variate Multi-Generations Tree (UVMGTree) worked very well in anomaly identification of the time series datasets and outperformed OCSVM, iForest, SARIMA, and Elliptic Envelope. Our approach can have a profound impact on the predictive maintenance and health monitoring of the industrial systems across the domains where the operations team can save significant time and effort in handling false alarms.. [ABSTRACT FROM AUTHOR]

We propose in this paper a partial order framework for clustering incomplete data. The paramount feature of this framework is that it spans over a partial order that can be leveraged to establish data similarity. We present the underlying theoretical foundations and study the convergence of clustering algorithms in this framework. In addition, we present a partial order-based clustering algorithm (POK-means) that illustrates the embedding of K-means clustering algorithm in our framework. The first contribution of our method is that unlike methods based on imputation of the missing values, our method does not make any assumptions about missing data. Another important contribution is that it alleviates false dismissals caused by other interval-based similarity measures. The experimental results show that, although our method do not assume any prior knowledge of (or assumptions about) missing data, it is competitive to most of published incomplete data clustering methods that are based on assumptions about input data or imputation (e.g. methods based on partial or interval kernel distances) in accuracy and performance. [ABSTRACT FROM AUTHOR]

A preference–approval on a set of alternatives consists of a weak order on that set and, additionally, a cut-off line that separates acceptable and unacceptable alternatives. In this paper, we propose a new method for defining the distance between preference–approvals taking into account jointly the disagreements in preferences and approvals for each pair of alternatives. The proposed distance is compared to the existing distance functions to deal with clustering problems. Specifically, we prove that our metric improves the estimated clusters in terms of both stability and accuracy. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) have advanced significantly over the past years as an incredible technology for averting congested traffic and enhancing traffic safety. Recent researchers show that incorporating Wireless Sensor Networks (WSN) into ITS can decrease the necessary investment and permits the creation of intelligent collaborative applications that enhance traffic efficiency and driver safety. In this paper, we propose a Trust Aware Clustering Technique based on Rat Swarm Optimizer for WSN-based Intelligent Transportation System (TACTIRSO), which is a secure method for selecting cluster heads (CHs) based on nodes' trust value. We employed the Rat Swarm Optimizer (RSO), one of the most recent swarm-based optimization methods, to efficiently choose CHs. For the selection of CH, the proposed fitness function takes into account the node remaining energy and trust value. Moreover, the exponential moving average model is employed to dynamically change the predefined threshold values according to the network state. In order to enhance the performance of RSO, we applied different local search strategies in addition to an energy and trust-aware method of initializing the rat population. The simulation results reveal that TACTIRSO outperforms existing studies in terms of energy efficiency, selection of most trustworthy nodes, and average network lifetime. Numerical results indicate that TACTIRSO improves average network stability by at least 1.13 times, average trust value by at least 3.31 times, and reliability by at least 4.45 times over other schemes in a heterogeneous network, while these improvements are by 1.02 times, 0.33 times, and 3.52 times, respectively, in a homogeneous network. [ABSTRACT FROM AUTHOR]

In existing deep clustering methods, it is assumed that all generated representations are equally important during the clustering procedure. However, if the model can't learn proper cluster-oriented representations, all generated representations may not be suitable for clustering. In this case, some important representations need to be more effective than the other representations in forming optimal clusters. The existing deep clustering methods do not support this idea. Also, in most methods, Kullback–Leibler Divergence (KLD) loss function is used. KLD does not preserve global data structure. In this paper, an efficient joint deep clustering framework, termed EDCWRN, is introduced to learn representations and cluster labels, simultaneously. To overcome the mentioned problems, in EDCWRN, an automatic local representation weighting strategy is applied to weight the representations of each cluster properly. Moreover, the samples and their neighbors are involved in the learning representations procedure to generate better representation. Also, a new efficient formulation for cluster assignments is proposed. Using this formulation, global and local data structure is preserved simultaneously. Experiments show that the proposed model is more efficient than other state-of-the-art methods. The implementation-source code- of EDCWRN is made publicly available at https://github.com/Amin-Golzari-Oskouei/EDCWRN. [ABSTRACT FROM AUTHOR]

Due to the promotion of sustainable development policies, solar water heaters have quickly occupied the market. Choosing the best-performing solar water heater has become a popular group decision-making problem. This paper presents a best-worst large-scale group decision making (LSGDM) approach based on hesitant fuzzy linguistic preference relations (HFLPRs) and applies it to the selection of solar water heaters. First, we establish an optimization model to normalize hesitant fuzzy linguistic term sets (HFLTSs) with diverse lengths. Then, a satisfaction degree function based on the additive consistency of HFLPRs is proposed, and the fluctuation deviation level of HFLPRs is described. Next, we develop a clustering process combining the satisfaction degree and fluctuation deviation level to classify large-scale experts into several subgroups. The clustering algorithm not only improves the fusion of subgroups but also enhances the satisfaction of subgroups. Moreover, a best-worst model based on the credibility measure of subgroups is presented to determine the weights of the subgroups. Finally, a numerical example of a solar water heater which involves normalizing the HFLPRs given by 20 experts into HFLTSs with a length of 3, clustering the 20 experts into 7 subgroups with weights of 0.5153, 0.2125, 0.1332, 0.0155, 0.0854, 0.0287 and 0.0094, and selecting P1 as the most satisfactory alternative and a comparative analysis are utilized to demonstrate the availability of the proposed method. [ABSTRACT FROM AUTHOR]

Complex networks are practical tools for modeling, studying, and analyzing complex interactions between objects. These tools are essential in understanding applications, end-users, interactions between compute nodes, and their behaviors in computer networks. Computer networks are undergoing significant expansion due to the proliferation of network devices and compute nodes. One of the main challenges in computer networks is categorizing these compute nodes into clusters of connected compute nodes within these large-scale structures sharing similar features (e.g., Central Processing Unit, memory, disk storage). This paper proposes a set of novel, dynamic, and proactive topology-aware clustering approaches, namely, an Integer Linear Program, chemical reaction optimization, and a game theory approach that leverages the Irving algorithm, originally proposed to solve the stable roommate problem, to form clusters based on the compute nodes' features and their topological structures. Our proposed techniques are suggested to reduce the search space concerning Network Function Virtualization, Cloud-based Networks deployment, and to build on-demand clusters to meet their requirements. In this regard, the solutions aim to help decision-makers facing issues related to scalability and computational complexities of their mechanisms to deploy their cloud-based services effectively. Experimental results demonstrate the proposed approaches' effectiveness and suitability, given their polynomial-time complexities, making them easy to integrate into cloud providers' orchestration systems compared to K-Means and Density-based spatial clustering of applications with noise. [ABSTRACT FROM AUTHOR]