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Soft Subspace Based Ensemble clustering for Multivariate Time Series Data

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第10期34卷 7761-7774页

作者： He, Guoliang Jiang, Wenjun Peng, Rong Yin, Ming Han, Min Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Dalian Univ Technol Sch Elect Informat & Elect Engn Dalian 116024 Peoples R China

Recently, multivariate time series (MTS) clustering has gained lots of attention. However, state-of-the-art algorithms suffer from two major issues. First, few existing studies consider correlations and redundancies between variables of MTS data. Second, since different clusters usually exist in different intrinsic variables, how to efficiently enhance the performance by mining the intrinsic variables of a cluster is challenging work. To deal with these issues, we first propose a variable-weighted K-medoids clustering algorithm (VWKM) based on the importance of a variable for a cluster. In VWKM, the proposed variable weighting scheme could identify the important variables for a cluster, which can also provide knowledge and experience to related experts. Then, a Reverse nearest neighborhood-based density Peaks approach (RP) is proposed to handle the problem of initialization sensitivity of VWKM. Next, based on VWKM and the density peaks approach, an ensemble clustering framework (SSEC) is advanced to further enhance the clustering performance. Experimental results on ten MTS datasets show that our method works well on MTS datasets and outperforms the state-of-the-art clustering ensemble approaches.

关键词： clustering algorithms clustering methods Time series analysis Principal component analysis Partitioning algorithms Linear programming Weight measurement Ensemble clustering hard subspace clustering multivariate time series (MTS) soft subspace clustering

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Consistency-Induced Multiview Subspace clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 832-844页

作者： Qin, Yalan Feng, Guorui Ren, Yanli Zhang, Xinpeng Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.

关键词： Optimization clustering algorithms Correlation Kernel clustering methods Partitioning algorithms Neural networks High-dimensional data multiview clustering sample assignment consistency (SAC) structural consistency (SC)

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Graph Enhanced Fuzzy clustering for Categorical Data Using a Bayesian Dissimilarity Measure

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第3期31卷 810-824页

作者： Zhang, Chuanbin Chen, Long Zhao, Yin-Ping Wang, Yingxu Chen, C. L. Philip Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 999078 Peoples R China Zhaoqing Univ Sch Comp Sci & Software Zhaoqing 526061 Peoples R China Northwestern Polytech Univ Sch Software Xian 710072 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Categorical data are widely available in many real-world applications, and to discover valuable patterns in such data by clustering is of great importance. However, the lack of a decent quantitative relationship among categorical values makes traditional clustering approaches, which are usually developed for numerical data, perform poorly on categorical datasets. To solve this problem and boost the performance of clustering for categorical data, we propose a novel fuzzy clustering model in this article. At first, by approximating the maximum a posteriori (MAP) estimation of a discrete distribution of data partition, a new fuzzy clustering objective function is designed for categorical data. The Bayesian dissimilarity measure is formulated in this objective to tackle the subtle relationships between categorical values efficiently. Then, to further enhance the performance of clustering, a novel Kullback-Leibler divergence-based graph regularization is integrated into the clustering objective to exploit the prior knowledge on datasets, for example, the information about correlations of data points. The proposed model is solved by the alternative optimization and the experimental results on the synthetic and real-world datasets show that it outperforms the classical and relevant state-of-the-art algorithms. We also present the parameter analysis of our approach, and conduct a comprehensive study on the effectiveness of the Bayesian dissimilarity measure and the KL divergence-based graph regularization.

关键词： clustering algorithms Probabilistic logic Bayes methods Manifolds Linear programming Kernel Estimation Bayesian methods categorical data fuzzy centroids fuzzy clustering graph Kullback-Leibler (K-L) divergence

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A Personalized Low-Rank Subspace clustering Method Based on Locality and Similarity Constraints for scRNA-seq Data Analysis

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第5期27卷 2575-2584页

作者： Qiao, Tian-Jing Liu, Jin-Xing Shang, Junliang Yuan, Shasha Zheng, Chun-Hou Wang, Juan Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China

Single-cell RNA sequencing (scRNA-seq) technology can provide expression profile of single cells, which propels biological research into a new chapter. clustering individual cells based on their transcriptome is a critical objective of scRNA-seq data analysis. However, the high-dimensional, sparse and noisy nature of scRNA-seq data pose a challenge to single-cell clustering. Therefore, it is urgent to develop a clustering method targeting scRNA-seq data characteristics. Due to its powerful subspace learning capability and robustness to noise, the subspace segmentation method based on low-rank representation (LRR) is broadly used in clustering researches and achieves satisfactory results. In view of this, we propose a personalized low-rank subspace clustering method, namely PLRLS, to learn more accurate subspace structures from both global and local perspectives. Specifically, we first introduce the local structure constraint to capture the local structure information of the data, while helping our method to obtain better inter-cluster separability and intra-cluster compactness. Then, in order to retain the important similarity information that is ignored by the LRR model, we utilize the fractional function to extract similarity information between cells, and introduce this information as the similarity constraint into the LRR framework. The fractional function is an efficient similarity measure designed for scRNA-seq data, which has theoretical and practical implications. In the end, based on the LRR matrix learned from PLRLS, we perform downstream analyses on real scRNA-seq datasets, including spectral clustering, visualization and marker gene identification. Comparative experiments show that the proposed method achieves superior clustering accuracy and robustness.

关键词： Kernel clustering methods Data models Data mining Robustness clustering algorithms Sequential analysis clustering local structure constraint low-rank representation scRNA-seq similarity constraint

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Mobility Support for MIMO-NOMA User clustering in Next-Generation Wireless Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第10期22卷 6011-6026页

作者： Naeem, Muhammad Kamran Abozariba, Raouf Asaduzzaman, Md Patwary, Mohammad Univ Wolverhampton Sch Engn Comp & Math Sci Wolverhampton WV1 1LY England Birmingham City Univ Sch Comp & Digital Technol Birmingham B4 7XG England Staffordshire Univ Sch Creat Arts & Engn Stoke On Trent ST4 2DE England

Non-Orthogonal Multiple Access (NOMA) is a promising technology for future-generation wireless systems, with potential to contribute to the improvement of spectral efficiency. NOMA groups users into clusters, based on channel gain-difference. However, user mobility continuously changes the channel gain, which often requires re-clustering. In this article, we study a set of re-clustering methods: arbitrary, one-by-one and Kuhn-Munkres assignment algorithm (KMAA), that expedite link re-establishment and keep the clusters interference-free, taking into account the mobility of users. The methods are applied to automatically dissociate identified users within clusters, when the gain-difference is lower than a given threshold, followed by re-association procedure, which integrates users into different clusters, maintaining an appropriate gain-difference. Experimental results show that the KMAA method improves efficiency and capacity through minimizing the number of re-clustering events, improving resource utilization, and lowering signaling overhead. Other sets of results highlight the throughput and outage probability gains of the KMAA method across a wide range of mobility scenarios. We also provide an analysis of the KMAA algorithm when applied to MIMO-NOMA, encompassing link resiliency and maintenance of average gain-difference, among users in clusters.

关键词： NOMA clustering algorithms Resource management Mobile computing Heuristic algorithms Throughput Next generation networking switched beamforming MIMO-NOMA NGN massive MIMO 6G

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SGAE: Stacked Graph Autoencoder for Deep clustering

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IEEE TRANSACTIONS ON BIG DATA 2023年第1期9卷 254-266页

作者： Xiao, Shunxin Wang, Shiping Guo, Wenzhong Fuzhou Univ Coll Comp & Data Sci Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou 350116 Peoples R China

Unsupervised clustering is a crucial issue in data mining and pattern recognition. Based on deep learning paradigms, deep clustering algorithms have been studied extensively and obtained superior performance in various applications. However, most of previous methods did not use helpful information from neighborhood relations to form group-separated space, and the feature embedding is usually distorted during the training process. To tackle the former limitation, we develop a graph convolution based unsupervised learning algorithm named Stacked Graph Autoencoder (SGAE). Specifically, SGAE utilizes the message passing mechanism to aggregate information from neighbors and obtain a meaningful and separated latent representation. Since the adjacency matrix is unavailable in clustering tasks, a graph construction approach with two pruning strategies is introduced to generate a transition matrix. To reduce the distortion caused by the multi-layered network training process, we further propose a topological structure preservation mechanism. It uses the constructed adjacency graph as supervised information, to maintain the relationship between nodes in the original space. Experiments on several popular benchmark datasets show that SGAE achieves significant improvements compared to unsupervised and semi-supervised deep clustering methods.

关键词： clustering algorithms Deep learning Big Data Training Convolution Task analysis Unsupervised learning clustering graph convolution stacked graph autoencoder deep unsupervised learning

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Gene Expression Data Analysis Using Feature Weighted Robust Fuzzy -Means clustering

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2023年第1期22卷 99-105页

作者： Singh, Vikas Verma, Nishchal K. K. IIT Dept Elect Engn Kanpur 208016 India

clustering of gene expression data has been proven to be very useful in various applications, i.e., identifying the natural structure inherent in gene expression, understanding gene functions, mining relevant information from noisy data, and understanding gene regulation. In all these applications, genes, i.e., features, play a crucial role in characterizing them into different groups. These features may be relevant, irrelevant, or redundant, but they have different contributions during the clustering process. This paper presents a novel approach by considering the effect of features during the clustering process. In the proposed method, the fuzzy ${c}$ -means the objective function is modified using a weighted Euclidean distance between the features with a monotonically decreasing function. The monotonically decreasing function helps control the features' contribution during the clustering process to partition the data into more relevant clusters. The proposed approach is validated, and performance is presented in various clustering performance measures on the different standard datasets. These clustering performance measures have also been compared with multiple state-of-the-art methods.

关键词： Gene expression clustering algorithms Partitioning algorithms Linear programming Weight measurement Nanobioscience Entropy K-medoids K-means alternate optimization cluster validation features fuzzy c-means gene expression

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Event Message clustering Algorithm for Selection of Majority Message in VANETs

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IEEE ACCESS 2023年 11卷 14621-14635页

作者： Khatri, Narayan Lee, Sihyung Mateen, Abdul Nam, Seung Yeob Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea Fed Urdu Univ Arts Sci & Technol Dept Comp Sci Islamabad 45570 Pakistan

The trustworthiness of nodes in Vehicular Ad-Hoc Networks (VANETs) is essential for disseminating truthful event messages. False messages may cause vehicles to behave in unintended ways, creating an unreliable transportation system. The efficiency and reliability of the transportation system can be obtained through trustworthy vehicular nodes providing correct event messages. In a VANET, the consensus issue can be resolved by employing blockchain. Even if we employ blockchain in a VANET, the trustworthiness of each message recorded needs to be verified separately since the blockchain itself does not guarantee the trust level of each event message. For instance, when there are multiple conflicting messages associated with a single accident on the road, a vote based on majority opinion can be considered one option for making a decision regarding the accident. In this work, we design the VANET event message clustering algorithm (VEMCA) to resolve the conflicting message problem. Furthermore, we develop a simulator for the VANET environment that demonstrates how the clustering algorithm can be used for event message validation. Experimental results show that our algorithm outperforms state-of-the-art clustering algorithms in terms of accuracy, precision, recall, f1-score, and computational time.

关键词： Vehicular ad hoc networks Peer-to-peer computing clustering algorithms Blockchains Trust management Safety Road traffic VANET clustering algorithm trustworthiness blockchain simulator

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Self-Constrained Spectral clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第4期45卷 5126-5138页

作者： Bai, Liang Liang, Jiye Zhao, Yunxiao Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Shanxi Peoples R China

As a leading graph clustering technique, spectral clustering is one of the most widely used clustering methods to capture complex clusters in data. Some additional prior information can help it to further reduce the difference between its clustering results and users' expectations. However, it is hard to get the prior information under unsupervised scene to guide the clustering process. To solve this problem, we propose a self-constrained spectral clustering algorithm. In this algorithm, we extend the objective function of spectral clustering by adding pairwise and label self-constrained terms to it. We provide the theoretical analysis to show the roles of the self-constrained terms and the extensibility of the proposed algorithm. Based on the new objective function, we build an optimization model for self-constrained spectral clustering so that we can simultaneously learn the clustering results and constraints. Furthermore, we propose an iterative method to solve the new optimization problem. Compared to other existing versions of spectral clustering algorithms, the new algorithm can discover a high-quality cluster structure of a data set without prior information. Extensive experiments on benchmark data sets illustrate the effectiveness of the proposed algorithm.

关键词： clustering algorithms Optimization Linear programming Kernel Computational efficiency Neural networks Deep learning Label constraints optimization model pairwise constraints self-constrained clustering spectral clustering

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Graph-Based Soft-Balanced Fuzzy clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第6期31卷 2044-2055页

作者： Liu, Chaodie Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

Spectral clustering have attracted more and more attention due to their well-defined mathematical frameworks and superior performance. However, there still exist two limitations to be solved: 1) most spectral clustering methods consist of two independent stages, which may cause unpredictable deviation of obtained clustering results from the genuine ones and lead to severe information loss and performance degradation;2) spectral clustering methods employ the hard clustering mode, which lacks interpretability for data points in the boundary area belonging to multiple clusters. To simultaneously address these challenging issues for spectral clustering, we propose a graph-based soft-balanced fuzzy clustering (GBFC) model. Specifically, we explicitly preserve the nonnegative property of the clustering indicator matrix to enhance the interpretability of clustering results. Moreover, row normalization is imposed on the cluster indicator matrix to show the membership of each data point to different clusters. Additionally, a novel balanced constraint is designed to regularize the clustering results and constrain the size of clusters. We can directly obtain the clustering assignments without any post-processing, and the limitations of the previous two-stage clustering framework can be effectively addressed. Extensive experiments performed on both synthetic datasets and real world datasets demonstrate the superiority and effectiveness of the proposed algorithm compared with several state-of-the-art methods.

关键词： clustering algorithms clustering methods Optimization Laplace equations Data models Partitioning algorithms Optics Fuzzy clustering graph clustering soft-balanced

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