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Information Theoretical Importance Sampling clustering and Its Relationship With Fuzzy C-Means

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 2164-2175页

作者： Zhang, Jiangshe Ji, Lizhen Wang, Meng Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Northwest China Grid Co Ltd Xian 710048 Peoples R China

A current assumption of most clustering methods is that the training data and future data are taken from the same distribution. However, this assumption may not hold in most real-world scenarios. In this article, we propose an information theoretical importance sampling based approach for clustering problems (ITISC), which minimizes the worst case of expected distortions under the constraint of distribution deviation. The distribution deviation constraint can be converted to the constraint over a set of weight distributions centered on the uniform distribution derived from importance sampling. The objective of the proposed approach is to minimize the loss under maximum degradation hence the resulting problem is a constrained minimax optimization problem, which can be reformulated as an unconstrained problem using the Lagrange method. The optimization problem can be solved by either an alternating optimization algorithm or a general optimization routine by commercially available software. Experiment results on synthetic datasets and a real-world load forecasting problem validate the effectiveness of the proposed model. Furthermore, we demonstrate that fuzzy c-means is a special case of ITISC with the logarithmic distortion, and this observation provides an interesting physical interpretation for fuzzy exponent m.

关键词： Optimization Monte Carlo methods clustering algorithms Distortion Synthetic data Load forecasting clustering methods Fuzzy c-means (FCM) importance sampling information theory minimax principle

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A Novel Fuzzy Hypersphere Neural Network Classifier Using Class Specific clustering for Robust Pattern Classification

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IEEE ACCESS 2024年 12卷 124209-124219页

作者： Mane, Deepak Kulkarni, Arun Pradhan, Biswajeet Gite, Shilpa Bidwe, Ranjeet Vasant Lee, Chang-Wook Alamri, Abdullah Vishwakarma Inst Technol Pune 411037 Maharashtra India Thadomal Shahani Engn Coll Mumbai 400050 Maharashtra India Univ Technol Sydney CAMGIS Sch Civil & Environm Engn Ultimo NSW 2007 Australia Symbiosis Int Deemed Univ SIU Symbiosis Inst Technol Pune Campus Pune 412115 Maharashtra India Symbiosis Int Deemed Univ Symbiosis Ctr Appl AI SCAAI Pune 412115 India Kangwon Natl Univ Dept Sci Educ Chuncheon Si 24341 Gangwon Do South Korea King Saud Univ Coll Sci Dept Geol & Geophys Riyadh 11421 Saudi Arabia

This paper introduces two novel class-specific fuzzy clustering algorithms: Mean-based Supervised clustering (MSC) and Density-based Mean Supervised clustering (DMSC). These algorithms are designed to construct the hidden layer of the Fuzzy Hypersphere Neural Network (FHNN) classifier, which is structured on the framework of the Radial Basis Function Neural Network (RBFNN). The FHNN classifier utilizes fuzzy sets as labeled pattern clusters in its hidden layer, with classes represented in the output layer formed by the aggregation of these fuzzy sets. An important characteristic of this classifier is its independence from tuning parameters. It meticulously determines centroids and radii for labeled clusters, consistently achieving 100% accuracy across any training set. The FHNN classifier effectively handles outliers and is robust to variations in data presentation, ensuring clear data visualization for users. During the creation of labeled clusters in the hidden layer, binary weight values are adjusted concurrently between the hidden and output layers. This study proposes the formation of fuzzy clusters with varying dimensions tailored to the dataset. The classifier architecture, rooted in the radial basis function neural network, achieves 100% training accuracy due to precise fuzzy cluster formation. Experimental comparisons with RBFNN and similar classifiers using sixteen benchmark datasets demonstrate the superiority of the proposed classifier in pattern recognition tasks.

关键词： clustering algorithms Training Classification algorithms Fuzzy sets Accuracy Pattern classification Neurons Fuzzy neural networks Radial basis function networks Fuzzy neural network fuzzy set hypersphere pattern classification radial basis function neural networks sep fuzzy membership function

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Fuzzy C-Means clustering and Improved Arithmetic Optimization Algorithm-Based Layering Cooperative Routing Protocol for UASNs

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IEEE SENSORS JOURNAL 2024年第15期24卷 24810-24824页

作者： Han, Duoliang Du, Xiujuan Liu, Xiuxiu Tian, Xiaojing Qinghai Normal Univ Dept Comp Xining 810008 Peoples R China Qinghai Normal Univ Dept Comp Qinghai Prov Key Lab IoT Xining 810008 Peoples R China State Key Lab Tibetan Intelligent Informat Proc & Xining 810008 Peoples R China

In underwater acoustic sensor networks (UASNs), the energy resources available to the underwater nodes are severely limited. The depletion of node energy can decrease network connectivity and shorten the lifetime of the network. clustering routing has been proven to be an effective technique for improving energy efficiency and extending the lifetime of the network. Therefore, concerning the problems of nonuniform clustering, unbalanced energy consumption, and short network lifetime in existing cluster routing protocols, a fuzzy C-means clustering and improved arithmetic optimization algorithm-based layering cooperative routing (FCALCR) protocol for 3-D static UASNs is proposed in this article. The FCALCR protocol introduces a fuzzy C-mean (FCM) algorithm to cluster the nodes in the network, which achieves uniform clustering. Each node is configured a layer to facilitate data packets to be transmitted layer by layer to the sink, which improves the reliability of the packet transmission. Furthermore, a Sobol sequence and multistage dynamic perturbation strategy-based arithmetic optimization algorithm (AOA) is proposed to select the best cluster head forwarding node, which accelerates the convergence speed and improves the robustness of the routing protocol. Simulation results show that the proposed FCALCR protocol outperforms the protocols of low-energy adaptive clustering hierarchy (LEACH), LEACH-expected residual energy (ERE), energy-efficiency grid routing based on 3D cubes (EGRC), FCM and moth flame optimization method (FCMMFO), and energy-efficient clustering routing protocol (EECRP) in terms of network lifetime and packet transmission efficiency.

关键词： Energy consumption Sensors Routing protocols clustering algorithms Routing Wireless sensor networks Arithmetic clustering cooperative routing fuzzy C-means (FCM) improved arithmetic optimization algorithm (AOA) network lifetime underwater acoustic sensor networks (UASNs)

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Cascaded Two-Stage Feature clustering and Selection via Separability and Consistency in Fuzzy Decision Systems

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第9期32卷 5320-5333页

作者： Chen, Yuepeng Ding, Weiping Ju, Hengrong Huang, Jiashuang Yin, Tao Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China City Univ Macau Fac Data Sci Macau 999078 Peoples R China

Feature selection is a vital technique in machine learning, as it can reduce computational complexity, improve model performance, and mitigate the risk of overfitting. However, the increasing complexity and dimensionality of datasets pose significant challenges in the selection of features. Focusing on these challenges, this article proposes a cascaded two-stage feature clustering and selection algorithm for fuzzy decision systems. In the first stage, we reduce the search space by clustering relevant features and addressing interfeature redundancy. In the second stage, a clustering-based sequentially forward selection method that explores the global and local structure of data is presented. We propose a novel metric for assessing the significance of features, which considers both global separability and local consistency. Global separability measures the degree of intraclass cohesion and interclass separation based on fuzzy membership, providing a comprehensive understanding of data separability. Meanwhile, local consistency leverages the fuzzy neighborhood rough set (FNRS) model to capture uncertainty and fuzziness in the data. The effectiveness of our proposed algorithm is evaluated through experiments conducted on 18 public datasets and a real-world schizophrenia dataset. The experiment results demonstrate our algorithm's superiority over benchmarking algorithms in both classification accuracy and the number of selected features.

关键词： clustering algorithms Rough sets Feature extraction Uncertainty Fuzzy systems Partitioning algorithms Classification algorithms Feature selection fuzzy decision systems fuzzy neighborhood rough set (FNRS) granular computing

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Spectral Embedding Fusion for Incomplete Multiview clustering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 4116-4130页

作者： Chen, Jie Chen, Yingke Wang, Zhu Zhang, Haixian Peng, Xi Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Northumberland England Sichuan Univ Law Sch Chengdu 610065 Peoples R China

Incomplete multiview clustering (IMVC) aims to reveal the underlying structure of incomplete multiview data by partitioning data samples into clusters. Several graph-based methods exhibit a strong ability to explore high-order information among multiple views using low-rank tensor learning. However, spectral embedding fusion of multiple views is ignored in low-rank tensor learning. In addition, addressing missing instances or features is still an intractable problem for most existing IMVC methods. In this paper, we present a unified spectral embedding tensor learning (USETL) framework that integrates the spectral embedding fusion of multiple similarity graphs and spectral embedding tensor learning for IMVC. To remove redundant information from the original incomplete multiview data, spectral embedding fusion is performed by introducing spectral rotations at two different data levels, i.e., the spectral embedding feature level and the clustering indicator level. The aim of introducing spectral embedding tensor learning is to capture consistent and complementary information by seeking high-order correlations among multiple views. The strategy of removing missing instances is adopted to construct multiple similarity graphs for incomplete multiple views. Consequently, this strategy provides an intuitive and feasible way to construct multiple similarity graphs. Extensive experimental results on multiview datasets demonstrate the effectiveness of the two spectral embedding fusion methods within the USETL framework.

关键词： Tensors Correlation Sparse matrices Optimization Matrix converters Filling clustering algorithms Incomplete multiview clustering low-rank tensor learning spectral embedding spectral rotation

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Anomaly Detection in Public Street Lighting Data Using Unsupervised clustering

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 4524-4535页

作者： Ali, Mubashir Scandurra, Patrizia Moretti, Fabio Sherazi, Hafiz Husnain Raza Univ Birmingham Edgbaston Comp Sci Dept Birmingham B15 2TT England Univ Bergamo Dept Engn & Appl Sci I-24044 Dalmine Italy ENEA Natl Agcy New Technol Energy & Econ Sustainab Energy Technol & Renewable Sources Dept I-00123 Rome Italy Newcastle Univ Sch Comp Newcastle Upon Tyne NE4 5TG England

The digitization of the energy industry enables the collection and analysis of vast amounts of consumption data for improved decision-making and efficient energy resource utilization. This big data represents an opportunity to address data quality issues and identify anomalies. The detection of anomalies in data that significantly deviates from normal behavior is critical, as they can lead to economic losses. To extract valuable insights and mitigate such challenges, big data analytics platforms and machine learning techniques are increasingly being adopted. Their practical use is crucial in defining consumer-centric, sustainable, and resilient technologies for Industry 5.0. In this article, we address the problem of anomaly detection in electricity consumption for public street lighting. We present our approach and experience in analyzing anomalous behavior of public street lighting plants using electric energy data collected and provided by the ENEA PELL smart city platform. To this end, we adopt three well-known unsupervised clustering techniques: k-means, DBSCAN, and OPTICS. We conduct a comparative analysis of these methods using a real benchmark data set and additional data with known anomalies, used as ground truth data, to verify the correctness of the methods. We synthetically generate additional data from the real data set by automatically injecting anomalies based on specific patterns of anomalous behavior identified for the public street lighting domain. We examine the clustering methods' performance by calculating their accuracy in detecting synthetic anomalies, as well as their computation cost. The experiments show that while K-means runs faster, DBSCAN is more accurate than K-means and OPTICS in detecting synthetic anomalies.

关键词： Lighting Anomaly detection clustering algorithms Energy consumption Support vector machines Smart cities Behavioral sciences clustering techniques public street lighting PELL smart city platform big data analytics Industry 5.0

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clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

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IEEE ACCESS 2024年 12卷 13678-13698页

作者： Kannout, Eyad Grzegorowski, Marek Grodzki, Michal Nguyen, Hung Son Univ Warsaw Inst Informat PL-02097 Warsaw Poland

Recommender systems (RS) are substantial for online shopping or digital content services. However, due to some data characteristics or insufficient historical data, may encounter considerable difficulties impacting the quality of their recommendations. This study introduces the clustering-based frequent pattern mining framework for recommender systems (clustering-based FPRS) - a novel RS constituting several recommendation strategies leveraging agglomerative clustering and FP-growth algorithms. The developed strategies combine the generated frequent itemsets with collaborative- and content-filtering methods to address the cold-start problem, which occurs whenever a new user or item enters the system. In such cases, the RS has limited information about the new user or object. Thus, the recommendations may be inaccurate. The experimental evaluation on several benchmark datasets showed that clustering-based FPRS is superior to state-of-the-art and could effectively alleviate the cold-start problem.

关键词： Recommender systems Filtering Data mining Predictive models Collaborative filtering clustering algorithms Task analysis Cold-start problem recommender system frequent pattern mining clustering

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Tensor Low-Rank Graph Embedding and Learning for One-Step Incomplete Multi-View clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 9763-9775页

作者： Wan, Minghua Zhu, Jingyu Sun, Chengli Yang, Zhangjing Yin, Jun Yang, Guowei Nanjing Audit Univ Sch Comp Sci Sch Intelligent Audit Nanjing 211815 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Nanjing Xiaozhuang Univ Key Lab Intelligent Informat Proc Nanjing 211171 Peoples R China Guangzhou Maritime Univ Sch Informat & Commun Engn Guangzhou 510700 Peoples R China Qingdao Univ Sch Elect Informat Qingdao 266071 Peoples R China

Traditional multi-view algorithms typically require data to be complete, and these algorithms may not be suitable or effective when dealing with incomplete data. As a result, the research field has witnessed the emergence of methods specifically designed for addressing incomplete multi-view clustering. In contrast, most of existing incomplete multi-view algorithms primarily emphasize capturing global information while neglecting the importance of local information. To overcome these challenges, we put forward a novel approach, named tensor low-rank graph embedding and learning for one-step incomplete multi-view clustering. We combine the beneficial aspects of graph embedding and tensor low-rank in this method, which not only focuses on local relationships but also provides insights into the global structure. Firstly, the initial similarity graph matrix is constructed using the inter-dependency between views. Secondly, the similarity graph matrix is structured as a third-order tensor and constrained by the tensor nuclear norm. This constraint enables the capturing of higher-order correlations across multiple views. Thirdly, graph embedding is added to obtain a common feature representation. Finally, the algorithm incorporates clustering to determine the optimal clustering labels. We conducted experiments comparing our algorithm with seven different incomplete multi-view methods using four different evaluation metrics. The experimental results indicate that our algorithm achieved the best clustering performance across five datasets under various missing rates. Especially when faced with a 50% missing rate in the Extended YaleB dataset, the clustering accuracy and normalized mutual information of our method are improved by 28.23% and 28.11%, respectively, compared to the second-best algorithm.

关键词： Tensors clustering algorithms clustering methods Correlation Computer science Symbols Sun Graph embedding incomplete multi-view multi-view clustering one-step tensor low-rank constraint

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Demodulation Scheme Against Phase Noise Using an Ensemble clustering Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 12938-12951页

作者： Li, Yu Zhang, Jiaheng Xie, Ning Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

Most wireless systems involve time-varying multipath channels, where the negative effect of the phase noise cannot be ignored. The phase noise can be introduced by imperfect phase-locked loop circuitry, imperfect channel estimation, or both. This paper considers the demodulation problem of wireless systems with phase noise. We propose an ensemble clustering algorithm to address the limitations of the existing demodulation schemes. The proposed ensemble clustering algorithm is named as the Ensemble clustering algorithm using the Matrix Factorization and Information Theory (MFIT-EC). The MFIT-EC algorithm improves the performance of existing ensemble clustering algorithms, which neglect different clustering effects of different base-clustering algorithms or different effects of different clusters of the same base-clustering algorithm. Based on the MFIT-EC algorithm, we design a demodulation scheme in a coherent wireless system with phase noise, referred to as the MFIT-EC demodulation scheme. Specifically, we first utilize several base-clustering algorithms to obtain different base-clustering results. Second, we use a weighted ensemble mechanism to allocate different weights to different base-clustering results, and calculate the certainty of the clusters of each base-clustering algorithm to obtain a better and more robust demodulation performance. We implement the proposed approach and conduct extensive performance comparisons through simulations, which show that the proposed approach has better performance than the prior approaches in terms of both clustering performance and demodulation performance.

关键词： clustering algorithms Demodulation Partitioning algorithms Wireless communication Phase noise Measurement Phase locked loops demodulation clustering ensemble clustering wireless communications

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Fast Multiview clustering by Optimal Graph Mining

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第9期35卷 13071-13077页

作者： Lu, Jitao Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence OPt & ElectroN iOPEN Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China

Multiview clustering (MVC) aims to exploit heterogeneous information from different sources and was extensively investigated in the past decade. However, far less attention has been paid to handling large-scale multiview data. In this brief, we fill this gap and propose a fast multiview clustering by an optimal graph mining model to handle large-scale data. We mine a consistent clustering structure from landmark-based graphs of different views, from which the optimal graph based on the one-hot encoding of cluster labels is recovered. Our model is parameter-free, so intractable hyperparameter tuning is avoided. An efficient algorithm of linear complexity to the number of samples is developed to solve the optimization problems. Extensive experiments on real-world datasets of various scales demonstrate the superiority of our proposal.

关键词： clustering algorithms Time complexity Optimization clustering methods Task analysis Mathematical models Learning systems Anchor graph graph cut multiview clustering (MVC) scalable clustering

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