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Clustering methods have become widely popular in the field of data analysis, as they enable the grouping of similar data points. However, the challenge of clustering high-dimensional data remains a significant obstacle due to the "curse of dimensionality." Traditional methods like Principal Component Analysis (PCA) and hierarchy-based feature agglomeration have been used to overcome this challenge by reducing the dimensionality of the dataset. However, there is currently no efficient method for clustering features of a high-dimensional dataset. To address this issue, we present a non-parametric clustering method in this paper, specifically designed for identifying features in a high-dimensional dataset. Our proposed feature clustering algorithm utilizes a partitioning algorithm that creates clusters of features. Through experimental results using publicly available datasets, we demonstrate the effectiveness of our method. Our approach offers a valuable tool for researchers and practitioners to better understand and analyze high-dimensional data. [ABSTRACT FROM AUTHOR]

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy Ⅽ-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy Ⅽ-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for the early detection of skin disorders, aiming to improve the prediction accuracy and precision. Our motivation lies in addressing the challenges posed by the uncertainty in skin lesion features. To achieve this, we employ fuzzy-based segmentation techniques to enhance the quality of skin disorder image processing. Additionally, we explore the integration of intensity data to further refine the segmented images. Our methodology consists of three main steps: segmentation, feature extraction, and classification. We begin by inputting the image into the segmentation phase, followed by the application of a fuzzy c-means clustering process to improve the segmentation output. From the resulting segmented image, we extract features using local binary pattern and local Gabor XOR pattern techniques. These features are then subjected to classification, employing a hybrid classifier that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The use of this hybrid classifier is particularly significant, as it leverages the strengths of both CNN and LSTM to enhance the predictive power of our model. Through rigorous experimentation, we have achieved promising results. Our proposed method exhibits an impressive accuracy of 94.6% and precision of 95.5% in comparison to the Conventional FCM Model, which only achieves an accuracy of 87% and precision of 82.5%. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy cluster-scaled principal component analysis (fuzzy cluster-scaled PCA) for mixed data, which consists of both numerical and categorical data with respect to quantitative and qualitative variables. The fuzzy cluster-scaled PCA has been proposed for high-dimension, low-sample size (HDLSS) data in which the number of variables (or dimensions) is much larger than the number of objects (or samples). In this case, the target data is only for the numerical data. The proposed fuzzy cluster-scaled PCA in this paper can be applied to the mixed type of HDLSS data by utilizing the feature of the fuzzy cluster-scaled correlation, which is decomposed into two parts: the first part is the correlation of classification structures between variables and the second part is the correlation between variables. Then, the first part can be adapted to the categorical data, and the second part can be used for the numerical data through the same objects. Several numerical examples used data from a survey concerned with the relationship between students' choice of devices and scores of examination/class marks to measure the educational effect show a better performance of this proposed method, and insightful, important information on educational effectiveness is clarified. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is an effective clustering approach that divides the dataset into fuzzy segments. Fuzzy clustering and outlier detection are two interconnected processes. Outlier detection is essential because it discloses hidden patterns and crucial information about a dataset and is beneficial in a wide range of applications like fraud detection, military surveillance, identifying computer network intrusions, image processing, insurance or health care, etc. Many fuzzy clustering techniques are robust to anomalies since they limit the outlier's impact on the cluster's centroid. In this paper, we have compared four fuzzy clustering techniques namely Fuzzy C-means (FCM), Noise Clustering (NC), Credibilistic Fuzzy C-means (CFCM), and Density Oriented Fuzzy C - Means (DOFCM) on the basis of some crucial properties that are essential for efficient outlier detection. To better evaluate the feasibility of all these techniques, we experimentally evaluated these techniques on six datasets (two Real and four Synthetic) in the presence of noise and outliers. The results of the comparative analysis show that DOFCM outperforms all other techniques in terms of outlier detection and cluster formation. This research will surely benefit any researcher willing to detect outliers in their study using fuzzy clustering or either implicitly or explicitly working on fuzzy clusters. [ABSTRACT FROM AUTHOR]

Fuzzy c -varieties (FCV) is a linear fuzzy clustering method, whose clustering criterion is constructed by replacing the fuzzy c -means (FCM) prototypes with linear varieties, and has been utilized for local principal component analysis. In this paper, the FCV iterative algorithm is modified for handling vertically distributed datasets under consideration of privacy preservation. A cryptographic calculation scheme is realized by utilizing the least square criterion without eigen solutions. The characteristics of the proposed algorithm is demonstrated through numerical experiments. [ABSTRACT FROM AUTHOR]

The paper concerns the use of fuzzy cognitive maps and k-means clustering to solve the problem of modeling multidimensional medical data. A fuzzy cognitive map is a recurrent neural network that describes the analyzed phenomenon in the form of key concepts and causal relationships between them. It is an effective tool for modeling decision support systems and is widely used in medicine. The aim of this paper is to analyze the use of fuzzy cognitive maps with k-means clustering to model decision support systems based on multidimensional data related to Parkinson's disease. K-means method was applied to group the data, and then a separate fuzzy cognitive map was built for each cluster to increase forecasting accuracy. The learning process was realized with the use of the previously developed Individually Directional Evolutionary Algorithm. The obtained results confirm that the analyzed approach provides much better forecasting accuracy than the standard approach based on one model. [ABSTRACT FROM AUTHOR]

Rapid development in microarray experiments leads toward generating a large amount of genes expressions. Handling such a massive amount of gene expressions is a challenge using traditional methods especially multiple missing expression values exist. This article proposed a novel hybrid fuzzy clustering mean and majority vote to yield better performance. The proposed method will be evaluated with other three imputation methods and trained with four publicly available microarray datasets. The experimental results show that the proposed method has promising results that able to replace the missing data as well as achieves high accuracy rates and low root mean square error values. [ABSTRACT FROM AUTHOR]

Within manufacturing processes, anomalies such as machinery faults and failures may lead to the outage situation of production lines. The outage of production lines is detrimental for the availability of production systems and may cause severe economic loss. To avoid the economic loss that may be caused by the outage situation, the prediction of anomalies on production lines is a crucial concern for manufacturers. Recently, data mining techniques have been applied to the manufacturing domain for predicting occurrence time of anomalies, such as the moment of machinery failure. However, existing predictive maintenance approaches have been limited to the prediction of the time of occurrence of machinery failures, while lacking the capability for identifying the criticality of the failures. This may lead to inappropriate maintenance plans and strategies. In this context, in this paper, we introduce a novel ontology-based approach to facilitate predictive maintenance in industry. The proposed approach is a combination use of fuzzy clustering and semantic technologies, where fuzzy clustering techniques are used to learn the criticality of failures based on machine historical data, and semantic technologies use the results of fuzzy clustering to predict the time of failures and the criticality of them. As results, a domain ontology for modeling predictive maintenance knowledge is developed, and a set of Semantic Web Rule Language (SWRL) predictive rules are proposed to reason about the time and criticality of machinery failures. A case study on a real-world industrial data set is followed to evaluate the usefulness and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract A new Fuzzy Hypersphere Neural Network (FHNN) classifier is proposed in the framework of Radial Basis Function Neural Network (RBFNN) for which two class specific fuzzy clustering algorithms are suggested for the creation of fuzzy set hyperspheres (FSHs) in the hidden layer and the traditional least mean square (LMS) algorithm used to determine the weights between hidden to output layer is avoided by assigning the appropriate binary weights during training. The FHNN classifier utilizes FSH as pattern cluster and classes are represented by union of FSHs. The proposed fuzzy clustering algorithms are developed on the basis of inter-class and intra-class fuzzy membership metrics with maximum coverage of data points to create the FSHs in the hidden layer of FHNN. The weights between hidden to output layer of FHNN are adapted simultaneously during the creation of FSHs in the hidden layer. Unlike RBFNN and other similar models, the learning is: fast, independent of tuning parameters, formulates precise centroids and widths or radii of the FSHs, leading to 100% training accuracy on any training set. The learning rule 1 proposed for the FHNN is insusceptible to the order of data presentation and always results in identical clusters showing good data visualization, while the learning rule 2 eradicates the overlap between inter-class FSHs. The performance of FHNN is compared with the RBFNNs using twelve benchmark data sets. The empirical findings demonstrate that the FHNN is highly efficient classifier. [ABSTRACT FROM AUTHOR]

Abstract Cluster analysis has been extensively applied in many areas, such as engineering, information science, science policy, life science, behavioral science, social science, and earth science. Currently, there is a demand for the classification of large amounts of complex data in order to both extract its latent structure represented by exploratory obtained clusters (or groups) and to summarize it. Since the central issue is this extraction and summarization, the focus of cluster analysis research has been to better capture a smaller number of clusters made up of similar data from the larger complex data then has previously been possible. However, if we observe a multiple number of datasets, and we need to obtain a single result from these datasets using cluster analysis, we must obtain clusters of similar data in a dataset while some external information in other datasets is balanced over the clusters. For example, if we consider that there are K workers and n working locations in which each location has a different working load, then we have two kinds of datasets; one is geographical data of n locations and the other is data of working load with respect to n locations. The purpose of the proposed cluster analysis is to obtain K clusters from the geographical dataset of n locations, while working loads of each cluster are homogeneous over the K clusters. In other words, we try to obtain clusters, each of which contains a balance of geographical distance and workload. Therefore, this paper proposes a cluster analysis that obtains homogeneous clusters in which each has similar data. A numerical example is given to show the good performance. [ABSTRACT FROM AUTHOR]

To prolong the life of wireless sensor networks(WSNs) and reduce energy consumption, a clustering protocol based on fuzzy logic for Multi-hop WSNs(FLCMN) is proposed. The algorithm considers the residual energy of nodes, the number of neighbor nodes and the average residual energy of neighbor nodes. According to the pre-set fuzzy rule base, the fuzzy system is used to evaluate the satisfaction of the elected cluster head(CH). In addition, the average residual energy of neighbor nodes is taken into consideration, which improves the hotspot problem and balances the energy consumption between clusters. At the same time, in order to improve the hot issues among clusters, a multi-hop transmission scheme based on Fibonacci sequence is proposed to prolong the lifetime of the network. Through simulation, FLCAMN algorithm outperforms LEACH, EAMMH and DFLC algorithms in terms of network’s survival time and energy consumption. [ABSTRACT FROM AUTHOR]

Nowadays, medical image processing is the most challenging and emerging field. Edge detection of MRI images is one of the most important stage in this field. The paper describes the proposed strategy to detect the edges of brain tumor from patient’s MRI scan images of the brain. At the first stage, this method includes some noise removal functions improving features that provides better characteristics of medical images for reliable diagnosis using Balance Contrast Enhancement Technique (BCET). The result of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method. Finally, Canny edge detection method is applied to detect the fine edges. During the experimental study, we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done using MATLAB software. The obtained results demonstrate some resistivity to a noise. Also, the accuracy of segmentation, in some cases of tumor pathology, was increased up to 10-15% regarding the expert estimates. [ABSTRACT FROM AUTHOR]

Vehicle classification has a significant use in traffic surveillance and management. There are many methods proposed to accomplish this task using variety of sensors. In this paper, a method based on fuzzy c-means (FCM) clustering is introduced that uses dimensions and speed features of each vehicle. This method exploits the distinction in dimensions features and traffic regulations for each class of vehicles by using multiple FCM clusterings and initializing the partition matrices of the respective classifiers. The experimental results demonstrate that the proposed approach is successful in clustering vehicles from different classes with similar appearances. In addition, it is fast and efficient for big data analysis. [ABSTRACT FROM AUTHOR]

Based on traditional FCM clustering algorithm, this paper proposed the data stream clustering algorithm based on FCM. When dealing with data stream, inheriting the membership degree matrix can make traditional FCM more effective for data stream. The conclusion is that the result of data stream FCM is basically same as traditional FCM. But some points with similar distances from different centers of the clusters are easily misclassified. In computing time, data stream FCM is more sensitive to the number of parts the data divided. But it isn’t so sensitive to the number of data arrives per time. [ABSTRACT FROM AUTHOR]

Fuzzy c-means algorithm (FCM) is a powerful clustering algorithm and it is widely used in image segmentation. However, in FCM, we need to give both the parameters of the number of clusters and the initial membership matrix in advance, and they affect the clustering performance heavily. In this paper, we propose a novel density based fuzzy c-means algorithm (D-FCM) by introducing density for each sample. The density peaks are used to determine the number of clusters and the initial membership matrix automatically. Experimental results on benchmark datasets and medical image segmentation datasets show the efficiency and effectiveness of our D-FCM. [ABSTRACT FROM AUTHOR]

Classical approaches to material synthesis rely on experimental results obtained by academicians and practitioners during a long period. However, a huge amount of data containing complex relationships mandates the use of computer-guided methods of material synthesis. Big data driven knowledge based models and other computational schemes allows to alternate hard experimental works. Such approaches provide ability of systematic and computationally effective analysis in order to predict composition, structure and related properties of new materials. In this paper we apply the fuzzy set theory to knowledge mining from big data on material characteristics. We propose fuzzy clustering-generated If-Then rules as a basis for computer synthesis of new materials. Validity of the proposed approach is verified on an example of prediction properties of Ti-Ni alloy. [ABSTRACT FROM AUTHOR]

A concept of “usuality” plays a great role in a commonsense theory. Usuality is a special case of a Z-number. The concept of U-number relates to the issue of reliability of information, especially in the realms of economics and decision analysis. In this study we consider clustering and statistical methods for identification of usual numbers from data. We consider operations with usuality numbers and provide as example a business problem used to illustrate this approach. [ABSTRACT FROM AUTHOR]

Research on offline signature recognition still has not shown satisfactory results as the results of recent research. Therefore this study aims to proposed an offline signature recognition and verification system which employed an efficient fuzzy Kohonen clustering networks (EFKCN) 1 algorithm. The proposed recognition system and signature verification system consist of five stages including data acquisition, image processing, data normalization, clustering, and evaluation. The recognition of signature patterns using the clustering method with the EFKCN algorithm shows relatively better result with 70% accuracy compared to the accuracy of previous research results 2 which is 53%, and a good signature recognition result can be developed to assist the verification system as well as the personal data verification system as made in this study. [ABSTRACT FROM AUTHOR]

The proposed modification of conventional fuzzy C-means clustering (FCM) algorithm aims to correct some of its shortcomings. We have focused on as missing flexibility in cluster number adaptation; limited cluster type grouping; less than optimal objective function for clusters of unequal size lying very close to each other; considerable computational time particularly in case of high dimensional data. With M&MFCM we propose to replace the usual Euclidean distance with Mahalanobis and Minkowski metrics in order to enhance the cluster detection capacity of FCM by allowing more accurate detection of arbitrary shapes of clusters for high dimensional datasets. Direct replacement of Euclidean distance in the objective function of FCM with Mahalanobis might cause numerical problems as the largest eigenvalues of the fuzzy covariance matrix could produce extremely long clusters thus contradicting the real data distribution. The improvement is achieved by fixing the ratio between the maximal and minimal eigenvalues of the covariance matrix. The parameterized Minkowski distance metric is adapted for implementation with FCM with various settings. We also propose an approach for improving the initial choice of cluster number and for visualization and analysis of cluster results for labeled and unlabeled datasets. Experimental results demonstrate that the proposed M&MFCM and test methodology significantly improve FCM clustering results. [ABSTRACT FROM AUTHOR]

This paper presents experimental results of cluster analysis using self organising neural networks for identifying failing banks. The paper first describes major reasons and likelihoods of bank failures. Then it demonstrates an application of a self-organising neural network and presents results of the study. Findings of the paper demonstrate that a self-organising neural network is a powerful tool for identifying potentially failing banks. Finally, the paper discusses some of the limitations of cluster analysis related to understanding of the exact meaning of each cluster. [ABSTRACT FROM AUTHOR]

The Self-Enforcing Network (SEN), which is a self-organized learning neural network, is introduced as a tool for clustering to define reference types in complex data. In order to achieve this, a cue validity factor is defined, which first steers the clustering of the data. Finding reference types allows the analysis and classification of new data. The results show that a user can influence the clustering of data by sEN, thus allowing the analysis of the data depending on specific interests. The described tool includes concrete examples with real clinical data and shows the potential of such a network for the analysis of complex data. [ABSTRACT FROM AUTHOR]

The report is devoted to the results of analysis of information and the establishment of the relationship between the factors influencing the efficiency of exploitation and performance indicators (turnaround time, coefficient of pump flow) using fuzzy clustering algorithm. The relationship between input and output variables was obtained, which can be expressed by fuzzy rules. [ABSTRACT FROM AUTHOR]

Forecasting of geological parameters is very important for decision making on investment to exploration of new hydrocarbon structures and fields. On the one hand, the complexity of this problem is originated from the nonlinearity and uncertainty of behavior of an ensemble of interrelated parameters changing with respect to the depth. This phenomenon is considered analogously to time series, where the depth plays the role of time. On the other hand, the available data are irregular over the depth, as represent different geological bodies with distinct properties. These features mandate necessity to consider multivariable time series of geological parameters with irregular intervals. In this paper, we consider multilag forecasting of five geological parameters over the depth. As a model of forecasting, fuzzy c-means based fuzzy if-then rules are used and this allows better capture of high complexity of the considered phenomena than the classical precise forecasting model. The experimental data show validity of the suggested approach. [ABSTRACT FROM AUTHOR]

Cluster analysis is an important exploratory tool which reveals underlying structures in data and organizes them in clusters (groups) based on their similarities. The fuzzy approach to the clustering problem involves the concept of partial memberships of the instances in the clusters, increasing the flexibility and enhancing the semantics of the generated clusters. Several fuzzy clustering algorithms have been devised like fuzzy c-means (FCM), Gustafson-Kessel, Gath-Geva, kernel-based FCM etc. Although these algorithms do have a myriad of successful applications, each of them has its stability drawbacks related to several factors including the shape and density of clusters, the presence of noise or outliers and the choices about the algorithm's parameters and cluster center initialization. In this paper we are providing a heterogeneous cluster ensemble approach to improve the stability of fuzzy cluster analysis. The key idea of our methodology is the application of different fuzzy clustering algorithms on the datasets obtaining multiple partitions, which in the later stage will be fused into the final consensus matrix. Finally we have experimentally evaluated and compared the accuracy of this methodology. [ABSTRACT FROM AUTHOR]

This paper presents a visualization of a result of fuzzy clustering. The feature of fuzzy clustering is to obtain the degree of belongingness of objects to fuzzy clusters so the result will be more commensurate with reality. In addition, the number of clusters requires less and the solution of the result will be more robust when compared with conventional hard clustering. In contrast, the fuzzy clustering result interpretation tends to be more complicated. Therefore, measuring the similarity (or dissimilarity) between a pair of fuzzy classification status of objects is important. In order to measure the similarity (or dissimilarity) mathematically, it is necessary to introduce a scale to the fuzzy clustering result. That is, the obtained solutions as a fuzzy clustering result must be in a metric space. In order to implement this, we have proposed multidimensional joint scale and cluster analysis. In this analysis, we exploit a scale obtained by multidimensional scaling. This paper clarifies that the multidimensional joint scale and cluster analysis introduces scale to the fuzzy clustering result and then the visualization of the fuzzy clustering result in the metric vector space has a theoretical mathematical meaning through the Euclidean distance structure. In this paper, this is shown by using several numerical comparisons with ordinary visualizations of the fuzzy clustering result. [ABSTRACT FROM AUTHOR]

MRI is one of the most eminent medical imaging techniques and segmentation is a critical stage in investigation of MRI images. FCM is most usually used techniques for image segmentation of medical image applications because of its fuzzy nature, where one pixel can belong to multiple clusters and which lead to better performance than crisp methods. Conventional FCM fail to perform well enough in the presence of noise and intensity inhomogeneity in MRI images. Various FCM variations like BCFCM, PFCM, SFCM, FLICM, MDFCM, FCM_S1, FCM_S2, TEFCM, RFCMK, WIPFCM and KWFLICM, have been proposed to overcome these predicament by using the spatial statistics available in the images. In this paper all these techniques, used for segmentation, are implemented and compared in terms of two classes of cluster validity functions, fuzzy partition and feature structure, on the basis of their performance for the noisy MRI images. All these FCM variants are analyzed in terms of Partition Coefficient, Partition Entropy, Time Complexity and Segmentation Accuracy. [ABSTRACT FROM AUTHOR]

A simple methodology is proposed to quantify operating parameters of urban transport system by means GPS technology of low cost. Parameters such as: travel-time, speed, and delay were analyzed at a grade-separated busway without overtaking and with traffic signals. The system has 10.3 km of exclusive platform, 13 Bus stops, 17 Intersections and 7 U-turns. A mistake up to 10% by distance increase was reduced substantially applying an algorithm. GPS data and corrective method were validated and state-space-speed models were obtained for the route characterization and can be useful to optimization. Fuzzy c-means clustering was used for speed dataset. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is useful clustering technique which partitions the data set in fuzzy partitions and this technique is applicable in many technical applications like crime hot spot detection, tissue differentiation in medical images, software quality prediction etc. In this review paper, we have done a comprehensive study and experimental analysis of the performance of all major fuzzy clustering algorithms named: FCM, PCM, PFCM, FCM-σ, T2FCM, KT2FCM, IFCM, KIFCM, IFCM-σ, KIFCM-σ, NC, CFCM, DOFCM. To better analysis their performance we experimented with standard data points in the presents of noise and outlier. This paper will act as a catalyst in the initial study for all those researchers who directly or indirectly deal with fuzzy clustering in their research work and ease them to pick a specific method as per the suitability to their working environment. [ABSTRACT FROM AUTHOR]

Statistical measure of finding Similar Sentences using a novel Fuzzy clustering algorithm framework is developed which organizes text from one or more documents into different clusters. The traditional fuzzy clustering approaches are not applicable to sentence clustering because most sentence similarity measures do not represent sentences in a common metric space. An enhanced Fuzzy clustering algorithm is applied in the sentence of datasets to group the related sentences. Page Rank algorithm highlights the more relevant inter clusters which interprets the Page-Rank score of an object. Expectation- Maximization (EM) framework has been developed in order to predict the overlapping clusters of semantically related sentences. Quotations dataset and News article dataset empirically implies the Similarity measure of matching Semantically Similar Sentences in which our system out performs the baseline method and projection methods. Our proposed method performs 34% higher in similarity scoring of related sentences. It also analyzes the clustering performance in terms of Entropy and Purity which yields more Purity and less Entropy. Our Experimental results demonstrates that our method is capable of identifying the overlapping clusters of semantically related sentences, and can be used in a variety of text mining tasks. [ABSTRACT FROM AUTHOR]

Radar signal sorting is a key technique in electronic reconnaissance systems and is currently an important research direction in radar signal processing. Clustering, one of data mining techniques, has been adopted to solve radar sorting problem. However, none of the existing clustering-based methods is able to perform real-time analysis on high speed radar stream. Therefore, in this paper, we propose, design and implement a high performance FPGA-based data stream mining system to perform real-time radar signal sorting on continuous data stream. Firstly, a density-based clustering algorithm is proposed for radar signal sorting; secondly, FPGA-based program of the clustering algorithm is designed and implemented; thirdly, the FPGA board is designed and implemented. Experiments are performed on the board we designed. The results show that the proposed system can achieve real-time radar signal sorting on FPGA, and the resource consumption on FPGA is very low. The clustering algorithm is efficient in terms of accuracy. [ABSTRACT FROM AUTHOR]

Image segmentation problem is a fundamental task and process in computer vision and image processing applications. It is well known that the performance of image segmentation is mainly influenced by two factors: the segmentation approaches and the feature presentation. As for image segmentation methods, clustering algorithm is one of the most popular approaches. However, most current clustering-based segmentation methods exist some problems, such as the number of regions of image have to be given prior, the different initial cluster centers will produce different segmentation results and so on. In this paper, we present a novel image segmentation approach based on DP clustering algorithm. Compared with the current methods, our method has several improved advantages as follows: 1) This algorithm could directly give the cluster number of the image based on the decision graph; 2) The cluster centers could be identified correctly; 3) We could simply achieve the hierarchical segmentation according to the applications requirement. A lot of experiments demonstrate the validity of this novel segmentation algorithm. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks, low latency, energy efficiency, and coverage problems are considered as three key issues in designing routing protocols. In this paper we present a new protocol called Low Energy Adaptive Tier Clustering Hierarchy (LEATCH), which offers a good compromise between delay and energy consumption and resolves some coverage problems. For our purpose, a two level hierarchical approach has been proposed to organize a sensor network into a set of clusters, every cluster divided into small clusters that are called Mini Clusters. As the way the clusters are organized, for each mini cluster we define a Mini Cluster- Head (MCH). Every MCH communicates with the cluster-head directly, it aggregates its mini-cluster information. In addition, we have made some changes in the procedure of cluster head and mini cluster head election. LEATCH promises better performances than the conventional LEACH protocol which is one of the most known hierarchical routing protocols using the probabilistic model to manage the energy consumption in WSNs. Simulation results show that LEATCH performs better than LEACH in term of energy, delay, coverage and scalability. [ABSTRACT FROM AUTHOR]

Given a set of n binary data points, a widely used technique is to group its features into k clusters (e.g. [7] ). In the case where n < k , the question of how overlapping are the clusters becomes of interest. In this paper we approach the question through matrix decomposition, and relate the degree of overlap with the sparsity of one of the resulting matrices. We present analytical results regarding bounds on this sparsity, and a heuristic to estimate the minimum amount of overlap that an exact grouping of features into k clusters must have. As shown below, adding new data will not alter this minimum amount of overlap. [ABSTRACT FROM AUTHOR]

Performance of a fuzzy expert system is related with how good the membership functions are normalized, tuned for a problem statement and correlation of antecedents and consequents. This paper helps in tuning and designing the membership functions that are best suited for the problem statement by integrating subtractive clustering method for fuzzy expert system design. Subtractive clustering algorithm is used to generate the tuned membership functions automatically in accordance to the domain knowledge. The proposed integrated design of clustering based fuzzy expert system acts in improving the accuracy and leads to a précised decision making environment. A practical example for ageing assessment of transformer insulation oil has been included to illustrate the method much effectively; the discussed example has been validated practically in the laboratory and is found that the proposed method is efficient in decision making. [ABSTRACT FROM AUTHOR]

This paper proposes a multidimensional cluster scaling (MDCS) which obtains scaling of multidimensional clus- ters (or groups). Conventional multidimensional scaling is well known as a method to obtain the scaling of multidi- mensional objects. However, there is no method to obtain the scaling of the clusters. The merit of this scaling method is its applicability for the analysis of large amounts of data such as big data. Since the purpose of the scaling method is to obtain the latent structure of a given data in a lower dimensional space in order to summarize the data features and the visualization of the data structure, for large amounts of data, a loss of the data information through the reduction of the dimensions has been a main problem with the use of the scaling method. The proposed method can solve this problem by the use of clusters of objects. Several numerical examples show the better performance of the proposed method. [ABSTRACT FROM AUTHOR]

The classification of objects by parameters of the bottomhole zone treatment by polymeric systems has been performed and the combinations of polymer and geological conditions with various levels of efficiency allowing making decisions with insufficient information have been obtained on the basis of fuzzy cluster analysis. [ABSTRACT FROM AUTHOR]

The approach to identify clusters of genes represented both by expression values and Gene Ontology annotations, where cluster membership should not be in conflict with any of the representations is presented in the paper. The method enables to identify the genes that are differently clustered in different representations, what can lead to further analysis and interesting conclusions. The approach is based on the fuzzy clustering algorithms and the notion of proximity as the aggregation operation at the higher level than similarity matrices is performed. The approach is verified on two datasets: a small synthetic and real-world gene dataset. [ABSTRACT FROM AUTHOR]

Abstract: Fuzzy co-clustering is a basic technique for revealing intrinsic co-cluster structures from cooccurrence information among objects and items. In most of fuzzy co-clustering algorithms, objects and items are partitioned based on different constraints. Objects are forced to be exclusively partitioned like as Fuzzy c-Means (FCM), while item memberships often represent just the relative significance of items in each cluster, i.e., items can be shared by multiple clusters. In a previous work, exclusive partition of items were achieved by introducing a penalty term in Fuzzy Clustering for Categorical Multivariate data (FCCM), which is an FCM-type co-clustering with entropy regularization mechanism. In this paper, the applicability of dual exclusive partition of objects and items are discussed in the frameworks of Fuzzy CoDoK and SCAD-based fuzzy co-clustering. [Copyright &y& Elsevier]

Abstract: The following paper discusses the use of a hybrid model for the prediction of short-term US interest rates. The model consists of a differential evolution-based fuzzy type-2 clustering with a fuzzy type-2 inference neural network, after input preprocessing with multiple regression analysis. The model was applied to forecast the US 3- Month T-bill rates. Promising model performance was obtained as measured using root mean square error. [Copyright &y& Elsevier]

Abstract: Large amounts and varieties of data have gained the interest of various industrial companies as well as fed the ever expanding need of the public for information exchange. At the same time, internet sites that are evaluated by target audiences have increased in number, so the public can visit the targeted web sites and evaluate them based upon their firsthand opinions. Since the public rather than experts are making these assessments, there will inevitably be evaluators with views contrary to other evaluators. In order to use the information from such preference assessments of web sites effectively, it is important to consider the accuracy of the estimation of this public opinion observed through web-based surveys. Therefore, we capture the latent features of the information, categorize subjects based on their preferences, and identify the obtained latent features to the categorized clusters. We propose a method to capture this latent structure of the evaluation data as fuzzy clusters, and through the fuzzy clusters to identify the features of the various categorized subjects. In addition, using the same scales of degree of belongingness of subjects to fuzzy clusters, temporal difference over the different industries are captured through the similarity of fuzzy clusters. We show a better performance by using numerical examples. [Copyright &y& Elsevier]

Abstract: This paper presents a fuzzy clustering based correlation which measures the similarity of the ordinary correlation of objects and the correlation of the classification structures of the objects. This correlation is derived from self- organized dissimilarity which measures dissimilarity of a pair of objects and classification structures of the objects. It is known that this dissimilarity performs better for noisy data. In this paper, we first describe the performance of the self-organized dissimilarity in the framework of the clustering model. Next, we show how to obtain the fuzzy clustering based correlation from the self-organized dissimilarity. Finally, we provide a numerical example of the use of fuzzy clustering based correlation. Our target data is high-dimension and low-sample size (HDLSS) data in which the number of variables is much larger than the number of objects. Recently the analysis of this type of data has gained tremendous interest. However, with this data, since we cannot obtain the correct solution as eigen-values of the covariance matrix of variables, we cannot obtain a result of the ordinary principal component analysis (PCA). We show that by exploiting the proposed fuzzy clustering based correlation, we can solve this problem and obtain the result of PCA with better performance. [Copyright &y& Elsevier]

Abstract: While many clustering techniques for interval-valued data have been proposed, there has been no proposal for a variable selection added fuzzy clustering method for high dimension low sample-size interval-valued data. This paper proposes such a novel fuzzy clustering method for interval-valued data with an adaptable variable selection. There are three reasons why the method is necessary: First, our target data in this study is high dimension low sample-size data. Due to the curse of dimensionality, we tend to obtain a poor classification result for this type of data. The main cause of this is noise occurring from irrelevant and redundant variables (dimensions). Therefore, we need to use an adaptable variable selection to reduce or summarize variables. Second, the merit of fuzzy clustering is to obtain the results with uncertain cluster boundaries, which is well adjusted with the uncertainty situation of classification to data. This gives a more robust result for the noise of data when compared with hard clustering while mathematically we can obtain a result with continuous values. Third, an adaptable representation of interval-valued data can be exploited to transform the original data into a more manageable data in order to avoid the curse of dimensionality. Numerical examples show a high performance for the proposed method. [Copyright &y& Elsevier]