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In this study, we present the Method of Spectral Redundancy Reduction (MSRR) for analyzing OES (optical emission spectroscopy) data of dry etching processes based on the principles of spectral clustering. To achieve this, the OES data are transformed into abstract graph matrices whose associated eigenvectors directly indicate anomalies in the data set. We developed an approach that allows for the reduction in temporally resolved optical emission spectra from plasma structuring processes in such a way that individual emission lines can be algorithmically detected, which exhibit a temporal behavior different from the collective behavior of the temporally resolved overall spectrum. The proportion of emission lines that behave consistently throughout the entire process duration is not considered. Our work may find applications in which OES is used as a process-monitoring technique, especially for low-pressure plasma processing. The major benefit of the developed method is that the scale of the original data is kept, making physical interpretations possible despite data reductions.

Multimodal multi-objective problems refer to situations where more than one Pareto set in the decision space corresponds to the same Pareto front in the objective space. In this paper, we propose a multimodal multi-objective coati optimization algorithm based on spectral clustering (MMOCOA-SC) for use in multimodal multi-objective problems. The algorithm starts by creating stable subpopulations via a spectral clustering method, grouping similar individuals within each subpopulation. Then, an improved coati optimization algorithm is proposed in order to increase the diversity of the population and continuously search for near-Pareto optimal solutions. Finally, we use an improved crowding distance method combined with a non-dominated sorting method to maintain and retain multiple near-Pareto optimal solutions. The MMOCOA-SC is evaluated alongside five state-of-the-art algorithms using the 2020 CEC test suite, IDMP test problems, and twelve classic engineering application problems. In comparison to the other five state-of-the-art algorithms, the experimental results show that MMOCOA-SC exhibits a superior performance when applied to the problems. This study explores multimodal multi-objective optimization algorithms with a focus on the concept of symmetry, which is crucial for multimodal multi-objective optimization problems in terms of achieving a balanced decision space and a diversity of solutions in the objective space.

Graph Neural Networks (GNNs) have gained popularity in image matching methods, proving useful for various computer vision tasks like Structure from Motion (SfM) and 3D reconstruction. A well-known example is SuperGlue. Lightweight variants, such as LightGlue, have been developed with a focus on stacking fewer GNN layers compared to SuperGlue. This paper proposes the h-GNN, a lightweight image matching model, with improvements in the two processing modules, the GNN and matching modules. After image features are detected and described as keypoint nodes of a base graph, the GNN module, which primarily aims at increasing the h-GNN’s depth, creates successive hierarchies of compressed-size graphs from the base graph through a clustering technique termed SC+PCA. SC+PCA combines Principal Component Analysis (PCA) with Spectral Clustering (SC) to enrich nodes with local and global information during graph clustering. A dual non-contrastive clustering loss is used to optimize graph clustering. Additionally, four message-passing mechanisms have been proposed to only update node representations within a graph cluster at the same hierarchical level or to update node representations across graph clusters at different hierarchical levels. The matching module performs iterative pairwise matching on the enriched node representations to obtain a scoring matrix. This matrix comprises scores indicating potential correct matches between the image keypoint nodes. The score matrix is refined with a ‘dustbin’ to further suppress unmatched features. There is a reprojection loss used to optimize keypoint match positions. The Sinkhorn algorithm generates a final partial assignment from the refined score matrix. Experimental results demonstrate the performance of the proposed h-GNN against competing state-of-the-art (SOTA) GNN-based methods on several image matching tasks under homography, estimation, indoor and outdoor camera pose estimation, and 3D reconstruction on multiple datasets. Experiments also demonstrate improved computational memory and runtime, approximately 38.1% and 26.14% lower than SuperGlue, and an average of about 6.8% and 7.1% lower than LightGlue. Future research will explore the effects of integrating more recent simplicial message-passing mechanisms, which concurrently update both node and edge representations, into our proposed model.

The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these partitioning methods can identify clusters of policies with similar attributes. The resulting partitions provide an actuarial framework for creating maps of dominant risks and unsupervised pricing grids. This research article aims to adapt well-established clustering methods to complex insurance datasets containing both categorical and numerical variables. To achieve this, we propose a novel approach based on Burt distance. We begin by reviewing the K-means algorithm to establish the foundation for our Burt distance-based framework. Next, we extend the scope of application of the mini-batch and fuzzy K-means variants to heterogeneous insurance data. Additionally, we adapt spectral clustering, a technique based on graph theory that accommodates non-convex cluster shapes. To mitigate the computational complexity associated with spectral clustering’s O(n3) runtime, we introduce a data reduction method for large-scale datasets using our Burt distance-based approach.

With the development of network technology, information networks have become one of the most important means for people to understand society. As the scale of information networks expands, the construction of network graphs and high-dimensional feature representation will become major factors affecting the performance of spectral clustering algorithms. To address this issue, in this paper, we propose a spectral clustering algorithm based on similarity graphs and non-linear deep embedding, named SEG_SC. This algorithm introduces a new spectral clustering model that explores the underlying structure of graphs through sparse similarity graphs and deep graph representation learning, thereby enhancing graph clustering performance. Experimental analysis with multiple types of real datasets shows that the performance of this model surpasses several advanced benchmark algorithms and performs well in clustering on medium- to large-scale information networks.

Subspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems. However, they often face challenges, such as omitting cluster information and the neglect of higher-order neighbor relationships within the data. To address these issues, a novel subspace clustering method named Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering is proposed. This method seamlessly embeds Markov transition probability information into the self-expression, leveraging a fine-grained neighbor matrix to uncover latent data structures. This matrix preserves crucial high-order local information and complementary details, ensuring a comprehensive understanding of the data. To effectively handle complex nonlinear relationships, the method learns the underlying manifold structure from a cross-order local neighbor graph. Additionally, connectivity constraints are applied to the affinity matrix, enhancing the group structure and further improving the clustering performance. Extensive experiments demonstrate the superiority of this novel method over baseline approaches, validating its effectiveness and practical utility.

To tackle the problem of the brain storm optimization (BSO) algorithm’s suboptimal capability for avoiding local optima, which contributes to its inadequate optimization precision, we developed a flock decision mutation approach that substantially enhances the efficacy of the BSO algorithm. Furthermore, to solve the problem of insufficient BSO algorithm population diversity, we introduced a strategy that utilizes the good point set to enhance the initial population’s quality. Simultaneously, we substituted the K-means clustering approach with spectral clustering to improve the clustering accuracy of the algorithm. This work introduced an enhanced version of the brain storm optimization algorithm founded on a flock decision mutation strategy (FDIBSO). The improved algorithm was compared against contemporary leading algorithms through the CEC2018. The experimental section additionally employs the AUV intelligence evaluation as an application case. It addresses the combined weight model under various dimensional settings to substantiate the efficacy of the FDIBSO algorithm further. The findings indicate that FDIBSO surpasses BSO and other enhanced algorithms for addressing intricate optimization challenges.

We use Category Theory to construct a ‘bridge’ relating directed graphs with undirected graphs, such that the notion of direction is preserved. Specifically, we provide an isomorphism between the category of simple directed graphs and a category we call ‘prime graphs category’; this has as objects labeled undirected bipartite graphs (which we call prime graphs), and as morphisms undirected graph morphisms that preserve the labeling (which we call prime graph morphisms). This theoretical bridge allows us to extend undirected graph techniques to directed graphs by converting the directed graphs into prime graphs. To give a proof of concept, we show that our construction preserves topological features when applied to the problems of network alignment and spectral graph clustering.

This study focuses on adapting spectral clustering, a numeric data-clustering technique, for categorical and mixed-type data. The method enhances spectral clustering for categorical and mixed-type data with novel kernel functions, showing improved accuracy in real-world applications. Despite achieving better clustering for datasets with mixed variables, challenges remain in identifying suitable kernel functions for categorical relationships.

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with this problem, this paper introduces a novel approach that combines marker-controlled watershed segmentation with a spectral clustering algorithm. Initially, we determined the local maxima within a series of variable windows according to the lower bound of the prediction interval of the regression equation between tree crown radius and tree height to preliminarily segment individual trees. Subsequently, using this geometric shape analysis method, the under-segmented trees were identified. For these trees, vertical tree crown profile analysis was performed in multiple directions to detect potential treetops which were then considered as inputs for spectral clustering optimization. Our experiments across six plots showed that our method markedly surpasses traditional approaches, achieving an average Recall of 0.854, a Precision of 0.937, and an F1-score of 0.892.

To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual information (PMI) graph kernel. The kernel is constructed according to the point-wise mutual information between nodes, which is then used as a proximity matrix to reconstruct the network and obtain the symmetric normalized Laplacian matrix. Finally, the network is partitioned by the eigendecomposition and eigenvector clustering of the Laplacian matrix. In addition, to determine the number of clusters during spectral clustering, this paper proposes a fast algorithm, BI-CNE, for estimating the number of communities. For a specific network, the algorithm first reconstructs the original network and then runs Monte Carlo sampling to estimate the number of communities by Bayesian inference. Experimental results show that the detection speed and accuracy of the algorithm are superior to other existing algorithms for estimating the number of communities. On this basis, the spectral clustering community detection algorithm PMIK-SC also has high accuracy and stability compared with other community detection algorithms and spectral clustering algorithms.

Multi-camera video surveillance has been widely applied in crowd statistics and analysis in smart city scenarios. Most existing studies rely on appearance or motion features for cross-camera trajectory tracking, due to the changing asymmetric perspectives of multiple cameras and occlusions in crowded scenes, resulting in low accuracy and poor tracking performance. This paper proposes a tracking method that fuses appearance and motion features. An implicit social model is used to obtain motion features containing spatio-temporal information and social relations for trajectory prediction. The TransReID model is used to obtain appearance features for re-identification. Fused features are derived by integrating appearance features, spatio-temporal information and social relations. Based on the fused features, multi-round clustering is adopted to associate cross-camera objects. Exclusively employing robust pedestrian reidentification and trajectory prediction models, coupled with the real-time detector YOLOX, without any reliance on supplementary information, an IDF1 score of 70.64% is attained on typical datasets derived from AiCity2023.

Community water fluoridation (CWF) adjusts fluoride levels in public water supplies to prevent tooth decay and promote dental health, irrespective of socioeconomic status or dental care access. Regular sampling by community water systems (CWS) ensures compliance with regulations and standards. The Centers for Disease Control and Prevention (CDC) provide biennial reports for health statistics surveillance by monitoring CWF status in US water systems. It is important to note that specific policies and practices related to CWF can vary between countries. Therefore, this research applies the spectral clustering method to group and analyze the reception of fluorinated water by CWS between populations in US states. The data from the National Water Fluoridation Statistics (2016–2018–2020) reported by the CDC have been considered. The spectral clustering approach identified five clusters of US states, which represent the different percentages of the population served by CWS receiving fluorinated water. Among the results, one cluster has the lowest value of the percentage (33.3%), and it includes Hawaii, New Jersey, Oregon, Idaho, Montana, Louisiana, New Hampshire, Alaska, and Utah. Conversely, the cluster of states including Ohio, Indiana, Maryland, South Dakota, Georgia, Virginia, North Dakota, Illinois, Minnesota, Kentucky, and the District of Columbia had the highest percentage (96.1%). These findings reveal relevant variations in the implementation of CWF across different US states, with some states having a notably lower percentage of their population receiving fluorinated water than others. This could inform policy and public health efforts to improve access to fluoridated water and enhance dental health outcomes in areas with lower coverage.

The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to be able to extract the desired information from images. In such cases, we are usually gently pushed towards the best possible clustering method, as it is often more robust than simple traditional image processing methods. We investigate the usefulness of combining two clustering methods for unsupervised image segmentation. We use the mixture models to extract the color and spatial image features based on the obtained output segments. Then we construct a similarity matrix (adjacency matrix) based on these features to perform spectral clustering. In between, we propose a label noise correction using Markov random fields. We investigate the usefulness of our method on many hand-crafted images of different objects with different shapes, colorization, and noise. Compared to other clustering methods, our proposal performs better, with 10% higher accuracy. Compared to state-of-the-art supervised image segmentation methods based on deep convolutional neural networks, our proposal proves to be competitive.

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets.

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.

Aspergillus series Versicolores are molds distributed among 17 species, commonly found in our environment, and responsible for infections. Since 2022, a new taxonomy has grouped them into 4 major lineages: A. versicolor, A. subversicolor, A. sydowii, and A. creber. Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) could be a faster and more cost-effective alternative to molecular techniques for identifying them by developing a local database. To evaluate this technique, 30 isolates from Aspergillus series Versicolores were used. A total of 59 main spectra profiles (MSPs) were created in the local database. This protocol enabled accurate identification of 100% of the extracted isolates, of which 97% (29/30) were correctly identified with a log score ≥ 2.00. Some MSPs recorded as Aspergillus versicolor in the supplier’s database could lead to false identifications as they did not match with the correct lineages. Although the local database is still limited in the number and diversity of species of Aspergillus series Versicolores, it is sufficiently effective for correct lineage identification according to the latest taxonomic revision, and better than the MALDI-TOF MS supplier’s database. This technology could improve the speed and accuracy of routine fungal identification for these species.

The urban road network is a large and complex system characterized by significant heterogeneity arising from different spatial structures and traffic demands. To facilitate effective management and control, it is necessary to partition the road network into homogeneous sub-areas. In this regard, we aim to propose a hybrid method for partitioning sub-areas with intra-area homogeneity, inter-area heterogeneity, and similar sizes, called CSDRA. It is specifically designed for bidirectional road networks with segment weights that encompass traffic flow, speed, or roadside facility evaluation. Based on community detection and spectral clustering, this proposed method comprises four main modules: initial partition, partitioning of large sub-areas, reassignment of small sub-areas, and boundary adjustment. In the preliminary partitioning work, we also design a road network reconstruction method which further helps to enhance the intra-area homogeneity and inter-area heterogeneity of partitioning results. Furthermore, to align with the requirement for comparable work units in practical traffic management and control, we control the similarity in the size of sub-areas by enforcing upper and lower bound constraints on the size of the sub-areas. We verify the outperformance of the proposed method by an experiment on the partitioning of an urban road network in Guangzhou, China, where we employ sidewalk barrier-free score data as segment weights. The results demonstrate the effectiveness of both the road network reconstruction method and the CSDRA proposed in this paper, as they significantly improve the partitioning outcomes compared with other methods using different evaluation indicators corresponding to the partitioning objectives. Finally, we investigate the influence of constraint parameters on the evaluation indicator. Our findings indicate that appropriately configuring these constraint parameters can effectively minimize sub-region size variations while having minimal impact on other aspects.

This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process for assessing the health of data distributions. This enhancement allows for a more robust handling of noisy sensor data, resulting in improved accuracy in health assessment. Secondly, the original limitation of assessing component health within the same parameter class is overcome by extending the evaluation to include multiple parameter classes. This extension leads to a more comprehensive assessment of satellite component health. Lastly, the method employs spectral clustering to determine the boundaries of different health status classes, offering an objective alternative to traditional expert-dependent approaches. By adopting this technique, the proposed method enhances the objectivity and accuracy of the health status classification. The experimental results show that the method is able to accurately describe the trends in the health status of components. Its effectiveness in real-time health assessment and monitoring of satellite components is confirmed. This research provides a valuable reference for further research on satellite component health assessment. It introduces novel and enhanced ideas and methodologies for practical applications.

Networks offer a compact representation of complex systems such as social, communication, and biological systems. Traditional network models are often inadequate to capture the diverse nature of contemporary networks, which may exhibit temporal variation and multiple types of interactions between entities. Multilayer networks (MLNs) provide a more comprehensive representation by allowing interactions between nodes to be represented by different types of links, each reflecting a distinct type of interaction. Community detection reveals meaningful structure and provides a better understanding of the overall functioning of networks. Current approaches to multilayer community detection are either limited to community detection over the aggregated network or are extensions of single-layer community detection methods with simplifying assumptions such as a common community structure across layers. Moreover, most of the existing methods are limited to multiplex networks with no inter-layer edges. In this paper, we introduce a spectral-clustering-based community detection method for two-layer MLNs. The problem of detecting the community structure is formulated as an optimization problem where the normalized cut for each layer is minimized simultaneously with the normalized cut for the bipartite network along with regularization terms that ensure the consistency of the within- and across-layer community structures. The proposed method is evaluated on both synthetic and real networks and compared to state-of-the-art methods. MLNs. The problem of detecting the community structure is formulated as an optimization problem where the normalized cut for each layer is minimized simultaneously with the normalized cut for the bipartite network along with regularization terms that ensure the consistency of the intra- and inter-layer community structures. The proposed method is evaluated on both synthetic and real networks and compared to state-of-the-art methods.

In contemporary wireless communication networks, base stations are organized into coordinated clusters (called cells) to jointly serve the users. However, such fixed systems are plagued by the so-called cell-edge problem: near the boundaries, the interference between neighboring clusters can result in very poor interference-to-signal power ratios. To achieve a high-quality service, it is an important objective to minimize the sum of these ratios over the cells. The most common approach to solving this minimization problem is arguably the spectral clustering method. In this paper, we propose a new clustering approach, which is deterministic and computationally much less demanding than current methods. Simulating on synthetic instances indicates that our methods typically provide higher quality solutions than earlier methods.

The Internet of Vehicles (IoV) enables vehicular data services and applications through vehicle-to-everything (V2X) communications. One of the key services provided by IoV is popular content distribution (PCD), which aims to quickly deliver popular content that most vehicles request. However, it is challenging for vehicles to receive the complete popular content from roadside units (RSUs) due to their mobility and the RSUs’ constrained coverage. The collaboration of vehicles via vehicle-to-vehicle (V2V) communications is an effective solution to assist more vehicles to obtain the entire popular content at a lower time cost. To this end, we propose a multi-agent deep reinforcement learning (MADRL)-based popular content distribution scheme in vehicular networks, where each vehicle deploys an MADRL agent that learns to choose the appropriate data transmission policy. To reduce the complexity of the MADRL-based algorithm, a vehicle clustering algorithm based on spectral clustering is provided to divide all vehicles in the V2V phase into groups, so that only vehicles within the same group exchange data. Then the multi-agent proximal policy optimization (MAPPO) algorithm is used to train the agent. We introduce the self-attention mechanism when constructing the neural network for the MADRL to help the agent accurately represent the environment and make decisions. Furthermore, the invalid action masking technique is utilized to prevent the agent from taking invalid actions, accelerating the training process of the agent. Finally, experimental results are shown and a comprehensive comparison is provided, which demonstrates that our MADRL-PCD scheme outperforms both the coalition game-based scheme and the greedy strategy-based scheme, achieving a higher PCD efficiency and a lower transmission delay.

Community detection can reveal unknown spatial structures embedded in spatial networks. Current spatial community detection methods are mostly modularity-based. However, due to the lack of appropriate spatial networks serving as a benchmark, the accuracy and effectiveness of these methods have not been tested sufficiently so far. This study first introduced a spatial autoregressive and gravity model united method (SARGM) to simulate benchmark spatial networks with known regional distributions. Then, a novel spectral clustering-based spatial community detection method (SCSCD) was proposed to identify spatial communities from eight kinds of benchmark spatial networks. Comparative experiments on SCSCD and three other methods showed that SCSCD performed the best in accuracy and effectiveness. Moreover, the scale parameter and the community number setting of the SCSCD were investigated experimentally. Finally, a case study was applied to the SCSCD to demonstrate its ability to extract the internal community structure of a high-speed train network in China.

Multi-view subspace clustering is an effective method that has been successfully applied to many applications and has attracted the attention of scholars. Existing multi-view subspace clustering seeks to learn multiple representations from different views, then gets a consistent matrix. Until now, most of the existing efforts only consider the multi-view information and ignore the feature concatenation. It may fail to explore their high correlation. Consequently, this paper proposes a multi-view subspace clustering algorithm with a novel consensus matrix construction strategy. It learns a consensus matrix by fusing the different information from multiple views and is enhanced by the information contained in the original feature direct linkage of the data. The error matrix of the feature concatenation data is reconstructed by regularization constraints and the sparse structure of the multi-view subspace. The feature concatenation data are simultaneously used to fuse the individual views and learn the consensus matrix. Finally, the data is clustered by using spectral clustering according to the consensus matrix. We compare the proposed algorithm with its counterparts on six datasets. Experimental results verify the effectiveness of the proposed algorithm.

Community detection is an important and powerful way to understand the latent structure of complex networks in social network analysis. This paper considers the problem of estimating community memberships of nodes in a directed network, where a node may belong to multiple communities. For such a directed network, existing models either assume that each node belongs solely to one community or ignore variation in node degree. Here, a directed degree corrected mixed membership (DiDCMM) model is proposed by considering degree heterogeneity. An efficient spectral clustering algorithm with a theoretical guarantee of consistent estimation is designed to fit DiDCMM. We apply our algorithm to a small scale of computer-generated directed networks and several real-world directed networks.

Complex networks arise in various fields, such as biology, sociology and communication, to model interactions among entities. Entities in many real-world systems exhibit different types of interactions, which requires modeling these type of systems properly. Multiplex networks are used to model these systems, as they can reflect the nodes’ pair-wise interactions as multiple distinct types of links across layers. Community detection is a widely studied application in network analysis as it provides insights into the structure and organization of the network. Even though multiple algorithms have been developed in the community detection field, many of them have a limited performance in the presence of noise. In this article, we develop a novel algorithm that combines tensor low-rank representation, spectral clustering and distance regularization to improve the accuracy in discovering communities in multiplex networks. The low-rank representation leads to reducing the noise and errors existing in the network and the optimization of an accurate consensus set of eigenvectors that reveals the communities in the network. Moreover, the proposed approach balances the agreement between the eigenvectors of each layer, i.e., individual subspaces, and the consensus set of eigenvectors, i.e., common subspaces, by minimizing the projection distance between them. The common and individual subspaces are computed efficiently through Tucker decomposition and modified spectral clustering, respectively. Finally, multiple experiments are conducted on real and simulated networks to evaluate the proposed approach and compare it to state-of-the-art algorithms. The proposed approach shows its robustness and efficiency in discovering the communities in multiplex networks.

Ranking sports teams generally relies on supervised techniques, requiring either prior knowledge or arbitrary metrics. In this paper, we offer a purely unsupervised technique. We apply this to operational decision-making, specifically, the controversial European Super League for association football, demonstrating how this approach can select dominant teams to form the new league. We first use random forest regression to select important variables predicting goal difference, which we use to calculate the Euclidian distances between teams. Creating a Laplacian eigenmap, we bisect the Fiedler vector to identify the natural clusters in five major European football leagues. Our results show how an unsupervised approach could identify four clusters based on five basic performance metrics: shots, shots on target, shots conceded, possession, and pass success. The top two clusters identify teams that dominate their respective leagues and are the best candidates to create the most competitive elite super league.

In recent years, air pollution has become one of the main factors harming the livable environment for human beings. Governments have recognized the importance of controlling air pollution and reached a consensus that regional joint control of air pollution is an effective means of dealing with environmental degradation. In this work, we focus on the impact of fine particulate matter and nitrogen compounds on the air quality of 14 prefecture-level cities in China’s Hunan Province using the insights of complex networks, and further propose a joint treatment scheme for these two pollutants. Multiscale temporal networks are constructed based on the height cross-correlation coefficient, which allows us to assess the variable network structures concerning different time scales. We use four network properties to assess the network structures for the pollutants. Through the Jensen-Shannon divergence of the probability distribution of these network attributes, it is found that NO2 affects AQI more in a short time interval than in a longer time interval. The correlation of both NO2 and PM2.5 among the 14 cities in about 15 days can best reflect the air quality in Hunan Province. In addition, instead of NO2, PM2.5 has become the culprit of air pollution in Hunan Province. The co-movement of the pollutants among the 14 cities is significant. The co-movement of the PM2.5 pollutants can last 45 days, while that of NO2 pollutants will gradually decrease over time. Furthermore, by using spectral clustering based on the network node correlation, we classify the 14 cities into five regions and two regions for PM2.5 and NO2, respectively. It provides a feasible implementation guide for the environmental governance of regional cooperation.

Spectral clustering algorithms are often used to find clusters in the community detection problem. Recently, a degree-corrected spectral clustering algorithm was proposed. However, it is only used for partitioning graphs which are generated from stochastic blockmodels. This paper studies the degree-corrected spectral clustering algorithm based on the spectral graph theory and shows that it gives a good approximation of the optimal clustering for a wide class of graphs. Moreover, we also give theoretical support for finding an appropriate degree-correction. Several numerical experiments for community detection are conducted in this paper to evaluate our method.

We consider the problem of modeling and estimating communities in directed networks. Models to this problem in the previous literature always assume that the sending clusters and the receiving clusters have non-overlapping property or overlapping property simultaneously. However, previous models cannot model the directed network in which nodes in sending clusters have overlapping property, while nodes in receiving clusters have non-overlapping property, especially for the case when the number of sending clusters is no larger than that of the receiving clusters. This kind of directed network exists in the real world for its randomness, and by the fact that we have little prior knowledge of the community structure for some real-world directed networks. To study the asymmetric structure for such directed networks, we propose a flexible and identifiable Overlapping and Non-overlapping model (ONM). We also provide one model as an extension of ONM to model the directed network, with a variation in node degree. Two spectral clustering algorithms are designed to fit the models. We establish a theoretical guarantee on the estimation consistency for the algorithms under the proposed models. A small scale computer-generated directed networks are designed and conducted to support our theoretical results. Four real-world directed networks are used to illustrate the algorithms, and the results reveal the existence of highly mixed nodes and the asymmetric structure for these networks.

Hyperspectral image (HSI) clustering is a challenging work due to its high complexity. Subspace clustering has been proven to successfully excavate the intrinsic relationships between data points, while traditional subspace clustering methods ignore the inherent structural information between data points. This study uses graph convolutional subspace clustering (GCSC) for robust HSI clustering. The model remaps the self-expression of the data to non-Euclidean domains, which can generate a robust graph embedding dictionary. The EKGCSC model can achieve a globally optimal closed-form solution by using a subspace clustering model with the Frobenius norm and a Gaussian kernel function, making it easier to implement, train, and apply. However, the presence of noise can have a noteworthy negative impact on the segmentation performance. To diminish the impact of image noise, the concept of sub-graph affinity is introduced, where each node in the primary graph is modeled as a sub-graph describing the neighborhood around the node. A statistical sub-graph affinity matrix is then constructed based on the statistical relationships between sub-graphs of connected nodes in the primary graph, thus counteracting the uncertainty image noise by using more information. The model used in this work was named statistical sub-graph affinity kernel graph convolutional subspace clustering (SSAKGCSC). Experiment results on Salinas, Indian Pines, Pavia Center, and Pavia University data sets showed that the SSAKGCSC model can achieve improved segmentation performance and better noise resistance ability.

With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM.

This paper describes a post-evaluation analysis of the system developed by ViVoLAB research group for the IberSPEECH-RTVE 2020 Multimodal Diarization (MD) Challenge. This challenge focuses on the study of multimodal systems for the diarization of audiovisual files and the assignment of an identity to each segment where a person is detected. In this work, we implemented two different subsystems to address this task using the audio and the video from audiovisual files separately. To develop our subsystems, we used the state-of-the-art speaker and face verification embeddings extracted from publicly available deep neural networks (DNN). Different clustering techniques were also employed in combination with the tracking and identity assignment process. Furthermore, we included a novel back-end approach in the face verification subsystem to train an enrollment model for each identity, which we have previously shown to improve the results compared to the average of the enrollment data. Using this approach, we trained a learnable vector to represent each enrollment character. The loss function employed to train this vector was an approximated version of the detection cost function (aDCF) which is inspired by the DCF widely used metric to measure performance in verification tasks. In this paper, we also focused on exploring and analyzing the effect of training this vector with several configurations of this objective loss function. This analysis allows us to assess the impact of the configuration parameters of the loss in the amount and type of errors produced by the system.

The current practice of adjusting hearing aids (HA) is tiring and time-consuming for both patients and audiologists. Of hearing-impaired people, 40–50% are not satisfied with their HAs. In addition, good designs of HAs are often avoided since the process of fitting them is exhausting. To improve the fitting process, a machine learning (ML) unsupervised approach is proposed to cluster the pure-tone audiograms (PTA). This work applies the spectral clustering (SP) approach to group audiograms according to their similarity in shape. Different SP approaches are tested for best results and these approaches were evaluated by Silhouette, Calinski-Harabasz, and Davies-Bouldin criteria values. Kutools for Excel add-in is used to generate audiograms’ population, annotated using the results from SP, and different criteria values are used to evaluate population clusters. Finally, these clusters are mapped to a standard set of audiograms used in HA characterization. The results indicated that grouping the data in 8 groups or 10 results in ones with high evaluation criteria. The evaluation for population audiograms clusters shows good performance, as it resulted in a Silhouette coefficient >0.5. This work introduces a new concept to classify audiograms using an ML algorithm according to the audiograms’ similarity in shape.

Hyperspectral image (HSI) clustering is a major challenge due to the redundant spectral information in HSIs. In this paper, we propose a novel deep subspace clustering method that extracts spatial–spectral features via contrastive learning. First, we construct positive and negative sample pairs through data augmentation. Then, the data pairs are projected into feature space using a CNN model. Contrastive learning is conducted by minimizing the distances of positive pairs and maximizing those of negative pairs. Finally, based on their features, spectral clustering is employed to obtain the final result. Experimental results gained over three HSI datasets demonstrate that our proposed method is superior to other state-of-the-art methods.

Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.

Convolutional neural networks (CNNs) have successfully achieved high accuracy in synthetic aperture radar (SAR) target recognition; however, the intransparency of CNNs is still a limiting or even disqualifying factor. Therefore, visually interpreting CNNs with SAR images has recently drawn increasing attention. Various class activation mapping (CAM) methods are adopted to discern the relationship between CNN’s decision and image regions. Unfortunately, most existing CAM methods are based on optical images; thus, they usually lead to a limiting visualization effect for SAR images. Although a recently proposed Self-Matching CAM can obtain a satisfactory effect for SAR images, it is quite time-consuming, due to there being hundreds of self-matching operations per image. G-SM-CAM reduces the time of such operation dramatically, but at the cost of visualization effect. Based on the limitations of the above methods, we propose an efficient method, Spectral-Clustering Self-Matching CAM (SC-SM CAM). Spectral clustering is first adopted to divide feature maps into groups for efficient computation. In each group, similar feature maps are merged into an enhanced feature map with more concentrated energy in a specific region; thus, the saliency heatmaps may more accurately tally with the target. Experimental results demonstrate that SC-SM CAM outperforms other SOTA CAM methods in both effect and efficiency.

With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.

According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.

In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, we develop a three-step methodology for the prompt detection of LOFA precursors (i.e., those combinations of component failures causing a LOFA). First, we randomly generate accident scenarios by Monte Carlo sampling of the failures of typical SMCCC components and simulate the corresponding transient system response by a deterministic thermal-hydraulic code. In this phase, we also employ quick-running Proper Orthogonal Decomposition (POD)-based Kriging metamodels, adaptively trained to reproduce the output of the long-running code, to decrease the computational time. Second, we group the generated scenarios by a Spectral Clustering (SC) employing the Fuzzy C-Means (FCM), in order to identify the main patterns of system evolution towards abnormal states (e.g., a LOFA). Third, we develop an On-line Supervised Spectral Clustering (OSSC) technique to associate time-varying parameters measured during plant functioning to one of the prototypical groups obtained, which may highlight the related LOFA precursors (in terms of SMCCC components failures). We apply the proposed technique to the simplified model of a cryogenic cooling circuit of a single module of the ITER Central Solenoid Magnet (CSM). The framework developed promptly detects 95% of LOFA events and around 80% of the related precursors.

Identification of subgroups of patients with chronic pain provides meaningful insights into the characteristics of a specific population, helping to identify individuals at risk of chronification and to determine appropriate therapeutic strategies. This paper proposes the use of spectral clustering (SC) to distinguish subgroups (clusters) of individuals with carpal tunnel syndrome (CTS), making use of the obtained patient profiling to argue about potential management implications. SC is a powerful algorithm that builds a similarity graph among the data points (the patients), and tries to find the subsets of points that are strongly connected among themselves, but weakly connected to others. It was chosen due to its advantages with respect to other simpler clustering techniques, such as k-means, and the fact that it has been successfully applied to similar problems. Clinical (age, duration of symptoms, pain intensity, function, and symptom severity), psycho-physical (pressure pain thresholds—PPTs—over the three main nerve trunks of the upper extremity, cervical spine, carpal tunnel, and tibialis anterior), psychological (depressive levels), and motor (pinch tip grip force) variables were collected in 208 women with clinical/electromyographic diagnosis of CTS, whose symptoms usually started unilaterally but eventually evolved into bilateral symmetry. SC was used to identify clusters of patients without any previous assumptions, yielding three clusters. Patients in cluster 1 exhibited worse clinical features, higher widespread pressure pain hyperalgesia, higher depressive levels, and lower pinch tip grip force than the other two. Patients in cluster 2 showed higher generalized thermal pain hyperalgesia than the other two. Cluster 0 showed less hypersensitivity to pressure and thermal pain, less severe clinical features, and more normal motor output (tip grip force). The presence of subgroups of individuals with different altered nociceptive processing (one group being more sensitive to pressure pain and another group more sensitive to thermal pain) could lead to different therapeutic programs.

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.

Profiling groups of patients in clusters can provide meaningful insights into the features of the population, thus helping to identify people at risk of chronification and the development of specific therapeutic strategies. Our aim was to determine if spectral clustering is able to distinguish subgroups (clusters) of tension-type headache (TTH) patients, identify the profile of each group, and argue about potential different therapeutic interventions. A total of 208 patients (n = 208) with TTH participated. Headache intensity, frequency, and duration were collected with a 4-week diary. Anxiety and depressive levels, headache-related burden, sleep quality, health-related quality of life, pressure pain thresholds (PPTs), dynamic pressure thresholds (DPT) and evoked-pain, and the number of trigger points (TrPs) were evaluated. Spectral clustering was used to identify clusters of patients without any previous assumption. A total of three clusters of patients based on a main difference on headache frequency were identified: one cluster including patients with chronic TTH (cluster 2) and two clusters including patients with episodic TTH (clusters 0–1). Patients in cluster 2 showed worse scores in all outcomes than those in clusters 0–1. A subgroup of patients with episodic TTH exhibited pressure pain hypersensitivity (cluster 0) similarly to those with chronic TTH (cluster 2). Spectral clustering was able to confirm subgrouping of patients with TTH by headache frequency and to identify a group of patients with episodic TTH with higher sensitization, which may need particular attention and specific therapeutic programs for avoiding potential chronification.

This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis. The effectiveness of spectral clustering stems from constructing an embedding space based on an affinity matrix. This matrix shows the pairwise similarity of the data points. Clustering is then obtained by determining the spectral decomposition of the Laplacian graph. In the manufacturing field, clustering is an essential strategy for fault diagnosis. In this study, an enhanced spectral clustering approach is presented, which is augmented with pairwise constraints, and that results in efficient identification of fault scenarios. The effectiveness of the proposed approach is described using a real case study about a diesel injection control system for fault detection.

Partitioning ocean flows into regions dynamically distinct from their surroundings based on material transport can assist search-and-rescue planning by reducing the search domain. The spectral clustering method partitions the domain by identifying fluid particle trajectories that are similar. The partitioning validity depends on the accuracy of the ocean forecasting, which is subject to several sources of uncertainty: model initialization, limited knowledge of the physical processes, boundary conditions, and forcing terms. Instead of a single model output, multiple realizations are produced spanning a range of potential outcomes, and trajectory clustering is used to identify robust features and quantify the uncertainty of the ensemble-averaged results. First, ensemble statistics are used to investigate the cluster sensitivity to the spectral clustering method free-parameters and the forecast parameters for the analytic Bickley jet, a geostrophic flow model. Then, we analyze an operational coastal ocean ensemble forecast and compare the clustering results to drifter trajectories south of Martha’s Vineyard. This approach identifies regions of low uncertainty where drifters released within a cluster predominantly remain there throughout the window of analysis. Drifters released in regions of high uncertainty tend to either enter neighboring clusters or deviate from all predicted outcomes.

Active distribution networks (ADNs) provide a flexible platform to integrate various distributed generation sources, among which the intermittent renewable sources impose high operating uncertainty. Topological flexibility of ADNs should be exploited to counter the stochastic operating conditions by modifying the topologies of ADNs. Quantifying the topological flexibility is a vital step to utilize it, which is lacking in previous studies. A quantification method is proposed to measure the topological flexibility of ADNs in this paper. First, the community structures of ADNs are detected to achieve spatial partitions of the networks. Second, an improved spectral clustering algorithm is employed to significantly reduce the dimensionality of the partition space, in which the ADNs are further partitioned using the affinity propagation algorithm. Finally, a topological flexibility metric is defined based on the guiding role of sectionalizing and tie switches within and between communities. The proposed topological flexibility quantification method is a superb approach to the utilization of flexibility resources in distribution networks. Case study results of test ADNs demonstrate the effectiveness and efficiency of the proposed quantification method.

Acoustic scene analysis has attracted a lot of attention recently. Existing methods are mostly supervised, which requires well-predefined acoustic scene categories and accurate labels. In practice, there exists a large amount of unlabeled audio data, but labeling large-scale data is not only costly but also time-consuming. Unsupervised acoustic scene analysis on the other hand does not require manual labeling but is known to have significantly lower performance and therefore has not been well explored. In this paper, a new unsupervised method based on deep auto-encoder networks and spectral clustering is proposed. It first extracts a bottleneck feature from the original acoustic feature of audio clips by an auto-encoder network, and then employs spectral clustering to further reduce the noise and unrelated information in the bottleneck feature. Finally, it conducts hierarchical clustering on the low-dimensional output of the spectral clustering. To fully utilize the spatial information of stereo audio, we further apply the binaural representation and conduct joint clustering on that. To the best of our knowledge, this is the first time that a binaural representation is being used in unsupervised learning. Experimental results show that the proposed method outperforms the state-of-the-art competing methods.

The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.

Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance. In the past years, a growing number of advanced hyperspectral remote sensing image classification techniques based on manifold learning, sparse representation and deep learning have been proposed and reported a good performance in accuracy and efficiency on state-of-the-art public datasets. However, most existing methods still face challenges in dealing with large-scale hyperspectral image datasets due to their high computational complexity. In this work, we propose an improved spectral clustering method for large-scale hyperspectral image classification without any prior information. The proposed algorithm introduces two efficient approximation techniques based on Nyström extension and anchor-based graph to construct the affinity matrix. We also propose an effective solution to solve the eigenvalue decomposition problem by multiplicative update optimization. Experiments on both the synthetic datasets and the hyperspectral image datasets were conducted to demonstrate the efficiency and effectiveness of the proposed algorithm.