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ShVEEGc: EEG clustering With Improved Cosine Similarity-Transformed Shapley Value

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第1期7卷 222-236页

作者： Li, Guanghui Shen, Jiahua Dai, Chenglong Wu, Hia Becker, Stefanie, I Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia Univ Queensland Sch Psychol St Lucia Qld 4072 Australia

As the number of unlabeled or mislabeled electroencephalogram (EEG) increases dramatically in such applications as cerebral disease diagnosis, rehabilitation, and brain-computer interfaces, the supervised approaches that require labels or markers become inapplicable. Unfortunately, there are few reports on unsupervised studies for unlabeled EEG data, especially for unlabeled EEG clustering. To address the challenging task, we propose an effective approach named ShVEEGc for EEG clustering inspired by an improved Shapley value in cooperative game theory. The idea of ShVEEGc is first utilizing an improved cosine similarity to measure the correlations of EEG data and then calculating the improved Shapley value based on the inherent connection between unlabeled EEG data, which considers both global connections and local relationships potentially hidden in EEG data. Thus, ShVEEGc not only has good anti-interference ability but also can mine potential relationships among unlabeled EEG data. The comparison experiments with fourteen state-of-the-art EEG time series clustering algorithms on eleven real-world EEG datasets with four standard evaluation criteria demonstrate the efficacy and superiority of ShVEEGc for EEG clustering. Besides, the discussion on the impact of several different similarity measures on ShVEEGc also illustrates that the improved cosine similarity proposed in this paper is more suitable for EEG data.

关键词： Electroencephalography Time series analysis clustering algorithms Time measurement clustering methods Feature extraction Games Cosine similarity cross-correlation EEG clus-tering shapley value

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Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical clustering

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IEEE ACCESS 2023年 11卷 13554-13563页

作者： Yang, Liulin Li, Chao Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China

The identification of vulnerable lines in smart grid systems is of great significance to increase the stability of the smart grid systems and reduce the occurrence of cascading fault blackouts. Inspired by the machine learning method, this study proposes a vulnerable line identification approach based on the improved agglomerative hierarchical clustering algorithm. By jointly considering the topological parameters and the electrical properties, we discuss the vulnerability of the transmission lines and establish the influencing factors. Then, we adopt principal component analysis (PCA) to select the influencing factors and reduce their dimensionality. Finally, an improved agglomerative hierarchical clustering algorithm is proposed and employed to divide the lines to identify the vulnerable lines in the smart grid systems. Experiments over the IEEE 39-bus system demonstrate that our proposed method can efficiently and accurately identify different types of potential vulnerable lines in smart grid systems.

关键词： Machine learning Load flow clustering algorithms Topology Power transmission lines Entropy Periodic structures Principal component analysis Smart grids Improved agglomerative hierarchical clustering vulnerable lines influencing factors machine learning principal component analysis (PCA) smart grid systems structure entropy

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Towards Adaptive Consensus Graph: Multi-View clustering via Graph Collaboration

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 6629-6641页

作者： Wang, Huibing Jiang, Guangqi Peng, Jinjia Deng, Ruoxi Fu, Xianping Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Liaoning Peoples R China Changzhou Univ Sch Comp & Artificial Intelligence Changzhou 213164 Jiangsu Peoples R China Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Hebei Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

Multi-view clustering is a long-standing important task, however, it remains challenging to exploit valuable information from the complex multi-view data located in diverse high-dimensional spaces. The core issue is the effective collaboration of multiple views to holistically uncover the essential correlations between multi-view data through graph learning. Furthermore, it is indispensable for most existing methods to introduce an additional clustering step to produce the final clusters, which evidently reduces the uniform relationship between graph learning and clustering. Based on the above considerations, in this paper, we present a novel method named multi-view clustering via graph collaboration (MCGC). Based on the low-dimensional representation space developed by MCGC, it first perceives the correlations between samples in each individual view under the supervision of the Hilbert-Schmidt independence criterion (HSIC). Then, MCGC proposes learning a consensus graph by adaptively collaborating between all the views, which is able to uncover the essential structure of the multi-view data. Meanwhile, by imposing the rank constraint on the Laplacian matrix of the consensus graph to partition the multi-view data naturally into the required number of clusters, the optimal clustering results can be obtained directly without any postprocessing steps. Finally, the resulting optimization problem is solved by an alternating optimization scheme with guaranteed fast convergence. Extensive experiments on 5 benchmark multi-view datasets demonstrate that MCGC markedly outperforms the state-of-the-art baselines.

关键词： Collaboration Correlation Task analysis Learning systems Data models clustering methods clustering algorithms Multi-view clustering consensus graph hilbert-schmidt independence criterion graph collaboration

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Spatiotemporal Sequence-to-Sequence clustering for Electric Load Forecasting

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IEEE ACCESS 2023年 11卷 5850-5863页

作者： Acquah, Moses Amoasi Jin, Yuwei Oh, Byeong-Chan Son, Yeong-Geon Kim, Sung-Yul Keimyung Univ Dept Elect Energy Engn Daegu 42601 South Korea Kyungpook Natl Univ Dept Elect & Elect Engn Daegu 41566 South Korea

Massive electrical load exhibits many patterns making it difficult for forecast algorithms to generalise well. Most learning algorithms produce a better forecast for dominant patterns in the case of weekday consumption and otherwise for less dominant patterns in weekend and holiday consumption. In view of this, there is the need to cluster the load patterns, so learning algorithms can focus on the patterns independently to produce forecasts with better accuracy for all cases. However, clustering time-series data breaks the time-series dependency, making model training difficult. This paper presents a novel sequence-to-sequence cluster framework to reform time-series dependency after clustering;this enables independent clusters to be modelled using Convolutional Neural Network-Gated Recurrent Unit, which learns spatiotemporal features for future forecasts. A real-world dataset by the Korea Power Exchange composed of nationwide consumption is used for case studies and experiments. Experimental results verify that the proposed study effectively improves the accuracy of electric load forecasting by about 50%, with a WAPE of 0.67%. The proposed method also speeds up the training process of the forecast algorithm by about 35%, given that only a subset of the dataset is trained due to clustering. Korea Water Resources Corporation has implemented the proposed method for load forecasting and system marginal price estimation.

关键词： Load forecasting Load modeling Predictive models Feature extraction clustering algorithms Forecasting Convolutional neural networks Convolutional neural network-gated recurrent unit (CNN-GRU) feature engineering k-means clustering LightGBM classifier sequence-to-sequence forecast short-term load forecast (STLF)

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Fast Markov clustering Algorithm Based on Belief Dynamics

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3716-3725页

作者： Li, Huijia Xu, Wenzhe Qiu, Chenyang Pei, Jian Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Nanjing Univ Finance & Econ Jiangsu Prov Key Lab Business Nanjing 210003 Peoples R China

Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new Markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes' normalized beliefs in complex networks. Second, we introduce a new Markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches O(TN) in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods.

关键词： Heuristic algorithms clustering algorithms Convergence Markov processes Broadcasting Computational complexity Trajectory Belief dynamics complex networks convergence large-scale networks Markov clustering algorithm

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Heterogeneous Regularization-Based Tensor Subspace clustering for Hyperspectral Band Selection

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第11期34卷 9259-9273页

作者： Huang, Shaoguang Zhang, Hongyan Xue, Jize Pizurica, Aleksandra Univ Ghent Dept Telecommun & Informat Proc B-9000 Ghent Belgium Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Univ Ghent Image Proc & Interpretat Lab IMEC Res Grp B-9000 Ghent Belgium

Band selection (BS) reduces effectively the spectral dimension of a hyperspectral image (HSI) by selecting relatively few representative bands, which allows efficient processing in subsequent tasks. Existing unsupervised BS methods based on subspace clustering are built on matrix-based models, where each band is reshaped as a vector. They encode the correlation of data only in the spectral mode (dimension) and neglect strong correlations between different modes, i.e., spatial modes and spectral mode. Another issue is that the subspace representation of bands is performed in the raw data space, where the dimension is often excessively high, resulting in a less efficient and less robust performance. To address these issues, in this article, we propose a tensor-based subspace clustering model for hyperspectral BS. Our model is developed on the well-known Tucker decomposition. The three factor matrices and a core tensor in our model encode jointly the multimode correlations of HSI, avoiding effectively to destroy the tensor structure and information loss. In addition, we propose well-motivated heterogeneous regularizations (HRs) on the factor matrices by taking into account the important local and global properties of HSI along three dimensions, which facilitates the learning of the intrinsic cluster structure of bands in the low-dimensional subspaces. Instead of learning the correlations of bands in the original domain, a common way for the matrix-based models, our model learns naturally the band correlations in a low-dimensional latent feature space, which is derived by the projections of two factor matrices associated with spatial dimensions, leading to a computationally efficient model. More importantly, the latent feature space is learned in a unified framework. We also develop an efficient algorithm to solve the resulting model. Experimental results on benchmark datasets demonstrate that our model yields improved performance compared to the state-of-the-art.

关键词： Tensors Correlation Data models Task analysis Hyperspectral imaging Computational modeling clustering algorithms Band selection (BS) hyperspectral image (HSI) remote sensing subspace clustering tensor

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Multi-View clustering With Graph Learning for scRNA-Seq Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第6期20卷 3535-3546页

作者： Wu, Wenming Zhang, Wensheng Hou, Weimin Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xidian Univ Key Lab Smart Human Comp Interact & Wearable Tech Xian 710071 Shaanxi Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang 050018 Hebei Peoples R China Northwest Minzu Univ Key Lab Chinas Ethn Languages & Informat Technol Minist Educ Lanzhou 730030 Gansu Peoples R China

Advances in single-cell biotechnologies have generated the single-cell RNA sequencing (scRNA-seq) of gene expression profiles at cell levels, providing an opportunity to study cellular distribution. Although significant efforts developed in their analysis, many problems remain in studying cell types distribution because of the heterogeneity, high dimensionality, and noise of scRNA-seq. In this study, a multi-view clustering with graph learning algorithm (MCGL) for scRNA-seq data is proposed, which consists of multi-view learning, graph learning, and cell type clustering. In order to avoid a single feature space of scRNA-seq being inadequate to comprehensively characterize the functions of cells, MCGL constructs the multiple feature spaces and utilizes multi-view learning to comprehensively characterize scRNA-seq data from different perspectives. MCGL adaptively learns the similarity graphs of cells that overcome the dependence on fixed similarity, transforming scRNA-seq analysis into the analysis of multi-view clustering. MCGL decomposes the networks of cells into view-specific and common networks in multi-view learning, which better characterizes the topological relationship of cells. MCGL simultaneously utilizes multiple types of cell-cell networks and fully exploits the connection relationship between cells through the complementarity between networks to improve clustering performance. The graph learning, graph factorization, and cell-type clustering processes are accomplished simultaneously under one optimization framework. The performance of the MCGL algorithm is validated with ten scRNA-seq datasets from different scales, and experimental results imply that the proposed algorithm significantly outperforms fourteen state-of-the-art scRNA-seq algorithms.

关键词： clustering algorithms Feature extraction Gene expression Optimization Graph neural networks Periodic structures Topology Graph learning multi-view clustering non-negative matrix factorization representation learning ScRNA-seq data

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NFLCS: An Service Function Chain Path Optimization Strategy Based on Network-Functional Layout clustering

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第8期72卷 10813-10825页

作者： Duan, Youxiang Lu, Yuxi Shen, Shigen Yu, Shui Zhang, Peiying Zhang, Wei Igorevich, Kostromitin Konstantin China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Natl Super Comp Ctr JinanShandong Prov Key Lab Co Jinan 250013 Peoples R China South Ural State Univ Dept Phys Nanoscale Syst Chelyabinsk 454080 Russia

Under the background of 5G and 6G super bandwidth and ultra-low delay, Software Defined Networking and Network Function Virtualization (SDN & NFV) makes the communication technology develop rapidly. Service Function Chain (SFC) selection is a crucial aspect of SDN & NFV, which faces many challenges of poor algorithm performance, high latency, and low profit. Moreover, most of the existing studies are stuck in optimizing for a single metric, which leads to a poor generalization of the proposed path optimization method. In this paper, we propose an SFC path optimization strategy based on the clustering of network functional layouts to address the urgent problem. First, we design a topological network identification algorithm, model the SFC path optimization problem as an attention problem and use a multi-headed attention mechanism to optimize different kinds of SFC layouts in a targeted manner. Among them, the multi-headed attention mechanism achieves the effective capture of multiple features in the SFC path selection problem compared with the traditional strategy. The experimental results demonstrate the effectiveness of Network-Functional Layout clustering Strategy (NFLCS) in optimizing SFC paths and show the ability to optimize the service function chain paths well in future complex network situations.

关键词： Optimization Layout Quality of service Load modeling clustering algorithms Service function chaining Data structures Service function chains attention mechanism network function virtualization path optimization

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A Survey of clustering algorithms for Big Data: Taxonomy and Empirical Analysis

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2014年第3期2卷 267-279页

作者： Fahad, Adil Alshatri, Najlaa Tari, Zahir Alamri, Abdullah Khalil, Ibrahim Zomaya, Albert Y. Foufou, Sebti Bouras, Abdelaziz RMIT Univ Sch Comp Sci & Informat Technol Melbourne Vic 3000 Australia Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW 2006 Australia Qatar Univ Dept Comp Sci Coll Engn Doha 2713 Qatar Al Baha Univ Dept Comp Sci Al Baha City 65431 Saudi Arabia

clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

关键词： clustering algorithms unsupervised learning big data

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Correntropy-Based Low-Rank Matrix Factorization With Constraint Graph Learning for Image clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第12期34卷 10433-10446页

作者： Zhou, Nan Choi, Kup-Sze Chen, Badong Du, Yuanhua Liu, Jun Xu, Yangyang Chengdu Univ Chengdu 610106 Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Sch Nursing Hong Kong Peoples R China Chengdu Univ Informat Technol Sch Automat Chengdu 610255 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Chengdu Univ Informat Technol Coll Appl Math Chengdu 610225 Peoples R China Rensselaer Polytech Inst Dept Math Sci Troy NY 12180 USA

This article proposes a novel low-rank matrix factorization model for semisupervised image clustering. In order to alleviate the negative effect of outliers, the maximum correntropy criterion (MCC) is incorporated as a metric to build the model. To utilize the label information to improve the clustering results, a constraint graph learning framework is proposed to adaptively learn the local structure of the data by considering the label information. Furthermore, an iterative algorithm based on Fenchel conjugate (FC) and block coordinate update (BCU) is proposed to solve the model. The convergence properties of the proposed algorithm are analyzed, which shows that the algorithm exhibits both objective sequential convergence and iterate sequential convergence. Experiments are conducted on six real-world image datasets, and the proposed algorithm is compared with eight state-of-the-art methods. The results show that the proposed method can achieve better performance in most situations in terms of clustering accuracy and mutual information.

关键词： Data models Laplace equations Convergence clustering algorithms Adaptation models Principal component analysis Image reconstruction Low-rank factorization machine learning maximum correntropy criterion (MCC) semisupervised learning (SSL)

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