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Multiview Deep Online clustering: An Application to Online Research Topic Modeling and Recommendations

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第5期10卷 2566-2578页

作者： Paul, Dipanjyoti Chakdar, Daipayan Saha, Sriparna Mathew, Jimson Indian Inst Technol Patna Dept Comp Sci & Engn Patna 801103 Bihar India

In today's scenario, a large number of scientific articles on various domains are being published everyday, resulting in a rapid change in the trends of research topics. Retrieving the trending topics, evaluating the trends and extracting the scope of topics could be beneficial to young researchers, which can be recommended for future scope. Publication of articles is a continuous process, and so is the evolution of topics as well as the scope. The dynamic behavior of topics can be handled by continuously updating the partitioning of incoming articles arriving in a streaming manner. This article proposes an online clustering approach utilizing the autoencoder, which is capable of handling the online stream of articles. The training is performed by considering the multiple views of articles and posing the topic modeling (TM) problem as a multiview (MV) clustering problem. In addition, we employ an evolutionary-based approach to the latent representation of data to automatically determine the number of clusters. To learn the nonlinear mapping appropriately and to generate clusters effectively, the model simultaneously optimizes the reconstruction loss and clustering loss in an MV framework. The developed method has experimented on ArXiv dataset to determine the trending topics, scope, and recommendation of topics. The superiority of the proposed method in generating clusters as well as in determining scope is shown by comparing the results with many existing and baseline methods.

关键词： clustering algorithms Data models Partitioning algorithms Market research Task analysis Decoding Computational modeling Autoencoder consensus partitioning multiview (MV) representation research topic recommendation scientific articles streaming data trending topics

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From Ensemble clustering to Subspace clustering: Cluster Structure Encoding

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第5期34卷 2670-2681页

作者： Tao, Zhiqiang Li, Jun Fu, Huazhu Kong, Yu Fu, Yun Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA MIT Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA ASTAR Inst High Performance Comp IHPC Singapore 138632 Singapore Rochester Inst Technol B Thomas Golisano Coll Comp & Informat Sci Rochester NY 14623 USA Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Khoury Coll Comp & Informat Sci Boston MA 02115 USA

In this study, we propose a novel algorithm to encode the cluster structure by incorporating ensemble clustering (EC) into subspace clustering (SC). First, the low-rank representation (LRR) is learned from a higher order data relationship induced by ensemble K-means coding, which exploits the cluster structure in a co-association matrix of basic partitions (i.e., clustering results). Second, to provide a fast predictive coding mechanism, an encoding function parameterized by neural networks is introduced to predict the LRR derived from partitions. These two steps are jointly proceeded to seamlessly integrate partition information and original features and thus deliver better representations than the ones obtained from each single source. Moreover, an alternating optimization framework is developed to learn the LRR, train the encoding function, and fine-tune the higher order relationship. Extensive experiments on eight benchmark datasets validate the effectiveness of the proposed algorithm on several clustering tasks compared with state-of-the-art EC and SC methods.

关键词： Encoding Codes clustering algorithms Predictive coding Optimization Training Sparse matrices Encoder network ensemble clustering (EC) higher order relationship subspace clustering (SC)

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Millimeter Channel clustering by Self-Organizing Maps With Time-Varying Topological Structure

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第2期71卷 1736-1746页

作者： Du, Fei Zhang, Yu Zhao, Xiongwen Geng, Suiyan Fu, Zihao Wang, Xiaoqing Yu, Lujia Li, Qingliang North China Elect Power Univ Sch Elect & Elect Engn State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Power Internet Things Technol Baoding 071003 Peoples R China China Res Inst Radiowave Propagat Natl Key Lab Electromagnet Environm Qingdao 266107 Peoples R China

Cluster-based channel model plays an important role in the fifth-generation (5G) and beyond 5G (B5G) wireless communication systems. A well-designed clustering algorithm can achieve a good compromise between the accuracy and complexity of the wireless channel model. The dependence on prior information of cluster, such as the number of clusters, is a common dilemma of current algorithms. In this article, a self-organizing map with time-varying topological structure (SOM-TVS) clustering algorithm is proposed. First, we combine the locations and weights of the neurons in competition layer and initialize the SOM according to multipath components (MPCs), in which the topology of the SOM is changed with the iterations of the algorithm. Then, a new MPCs' power weighted Gaussian-Sinc function is proposed to optimize the competitiveness performance of the algorithm. Finally, both the measured and simulated channels in millimeter wave (mmWave) are used to validate the performance of the proposed SOM-TVS algorithm. The SOM-TVS algorithm can perform great effectiveness and robustness based on numerical simulations and experimental validation results. More significantly, the proposed clustering algorithm does not require any prior knowledge of cluster and has a fairly low complexity.

关键词： Neurons clustering algorithms Topology Self-organizing feature maps Delays Channel models Wireless communication Channel measurement channel modeling clustering multipath component (MPC) self-organizing map with time-varying topological structure (SOM-TVS)

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Sliced Sparse Gradient Induced Multi-View Subspace clustering via Tensorial Arctangent Rank Minimization

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第7期35卷 7483-7496页

作者： Sun, Xiaoli Zhu, Rui Yang, Ming Zhang, Xiujun Tang, Yuanyan Shenzhen Univ Coll Math & Stat Shenzhen 518060 Peoples R China Westfield State Univeristy Dept Math & Comp Westfield MA 01086 USA Westfield State Univeristy Dept Informat Sci Westfield MA 01086 USA Shenzhen Polytech Sch Elect & Commun Engn Shenzhen 518055 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China

Multi-view clustering method tries to improve the performance of clustering by using the information existing in different views. The tensorial representation is more suitable to capture the high order correlations across different views while keep local geometrical structure in specific view. In this paper, we propose a sliced sparse gradient induced multi-view subspace clustering method via tensorial arctangent rank minimization, named SSG-TAR method. First, a tensorial arctangent rank (TAR) is defined, which is a tighter surrogate of the tensor rank and more effective to explore the consistency among multiple views. Second, a sliced sparse gradient regularization (SSG) is first proposed to enhance the discrimination between clusters and better capture the complementary information in view-specific feature space. Finally, we unify these two terms together and establish an efficient algorithm to optimize the proposed model. Furthermore, the constructed sequence was proved to converge to the stationary KKT point. We have carried out extensive experiments on ten datasets across different types and sizes to verify the performance of our model. The experimental results show that our method have achieved the state-of-the-art performance.

关键词： Tensors clustering algorithms Minimization clustering methods Laplace equations Correlation Sun Multiple view sliced sparse gradient tensor arctangent rank subspace clustering

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Learning an Optimal Bipartite Graph for Subspace clustering via Constrained Laplacian Rank

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 1235-1247页

作者： Nie, Feiping Chang, Wei Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Xian 710072 Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Xian 710072 Peoples R China

In this article, we focus on utilizing the idea of co-clustering algorithms to address the subspace clustering problem. In recent years, co-clustering methods have been developed greatly with many important applications, such as document clustering and gene expression analysis. Different from the traditional graph-based methods, co-clustering can utilize the bipartite graph to extract the duality relationship between samples and features. It means that the bipartite graph can obtain more information than other traditional graph methods. Therefore, we proposed a novel method to handle the subspace clustering problem by combining dictionary learning with a bipartite graph under the constraint of the (normalized) Laplacian rank. Besides, to avoid the effect of redundant information hiding in the data, the original data matrix is not used as the static dictionary in our model. By updating the dictionary matrix under the sparse constraint, we can obtain a better coefficient matrix to construct the bipartite graph. Based on Theorem 2 and Lemma 1, we further speed up our algorithm. Experimental results on both synthetic and benchmark datasets demonstrate the superior effectiveness and stability of our model.

关键词： Dictionaries Bipartite graph clustering algorithms Sparse matrices Laplace equations Data models Statistical learning Co-clustering structure Laplacian rank constraint optimal bipartite graph sparse coefficient subspace clustering

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Adversarial Multiview clustering Networks With Adaptive Fusion

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第10期34卷 7635-7647页

作者： Wang, Qianqian Tao, Zhiqiang Xia, Wei Gao, Quanxue Cao, Xiaochun Jiao, Licheng Xidian Univ Minist Educ Intellisense & Image Understanding State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Minist Educ Intellisense & Image Understanding Key Lab Xian 710071 Peoples R China Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen 518107 Peoples R China Xidian Univ Sch Artificial Intelligence Minist Educ Intellisense & Image Understanding Key Lab Xian 710071 Peoples R China

The existing deep multiview clustering (MVC) methods are mainly based on autoencoder networks, which seek common latent variables to reconstruct the original input of each view individually. However, due to the view-specific reconstruction loss, it is challenging to extract consistent latent representations over multiple views for clustering. To address this challenge, we propose adversarial MVC (AMvC) networks in this article. The proposed AMvC generates each view's samples conditioning on the fused latent representations among different views to encourage a more consistent clustering structure. Specifically, multiview encoders are used to extract latent descriptions from all the views, and the corresponding generators are used to generate the reconstructed samples. The discriminative networks and the mean squared loss are jointly utilized for training the multiview encoders and generators to balance the distinctness and consistency of each view's latent representation. Moreover, an adaptive fusion layer is developed to obtain a shared latent representation, on which a clustering loss and the ${l_{1,2}}$ -norm constraint are further imposed to improve clustering performance and distinguish the latent space. Experimental results on video, image, and text datasets demonstrate that the effectiveness of our AMvC is over several state-of-the-art deep MVC methods.

关键词： Feature extraction Image reconstruction Generators Data models clustering algorithms Training Representation learning Adaptive fusion adversarial training multiview clustering (MVC)

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Semi-Supervised clustering Under a "Compact-Cluster" Assumption

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第5期35卷 5244-5256页

作者： Jiang, Zhen Zhan, Yongzhao Mao, Qirong Du, Yang Jiangsu Univ Zhenjiang 212013 Jiangsu Peoples R China

Semi-supervised clustering (SSC) aims to improve clustering performance with the support of prior knowledge (i.e., side information). Compared with pairwise constraints, the partial labeling information is more natural to characterize the data distribution in a high level. However, the natural gap between the class information and the clustering is not adequately taken into account in exiting SSC methods when utilizing partial labeling information to guide the clustering procedure. In order to address this problem, we present a "compact-cluster" assumption for SSC to utilize the partial labeling information via a cluster-splitting technique. Based on this assumption, a general framework, CSSC, is proposed to supervise the traditional clustering with an objective function which is defined by incorporating an item to measure the compact degree of clusters. Furthermore, we provide two effective solutions for Kmeans and spectral clustering within the CSSC framework and derive the corresponding algorithms to seek the optimum number of clusters and their centroids. Corresponding theoretical analyses demonstrate the feasibility and effectivity of the proposed method. Finally, the extensive experiments on eight real-world datasets demonstrate the superiority of our method over other state-of-the-art SSC methods.

关键词： clustering algorithms Labeling Linear programming Partitioning algorithms Search problems Optimization Transforms Semi-supervised clustering partial labeling compact-cluster assumption cluster-splitting

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Privacy-Preserving and Outsourced Multi-Party K-Means clustering Based on Multi-Key Fully Homomorphic Encryption

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2023年第3期20卷 2348-2359页

作者： Zhang, Peng Huang, Teng Sun, Xiaoqiang Zhao, Wei Liu, Hongwei Lai, Shangqi Liu, Joseph K. Shenzhen Univ Coll Elect Guangdong Key Lab Intelligent Informat Proc Informat Engn Shenzhen 518060 Peoples R China Guangzhou Univ Inst Artificial Intelligence Blockchain Guangzhou 510006 Peoples R China Beihang Univ Beijing 100191 Peoples R China Shenzhen Inst Informat Technol Sch Comp Sci Shenzhen 518172 Peoples R China Shenzhen Technol Univ Coll Big Data Internet Shenzhen 518118 Peoples R China Monash Univ Fac Informat Technol Clayton Vic 3800 Australia

The clustering algorithm is a useful tool for analyzing medical data. For instance, the k-means clustering can be used to study precipitating factors of a disease. In order to implement the clustering algorithm efficiently, data computation is outsourced to cloud servers, which may leak the private data. Encryption is a common method for solving this problem. But cloud servers are difficult to calculate ciphertexts from multiple parties. Hence, we choose multi-key fully homomorphic encryption (FHE), which supports computations on the ciphertexts that have different secret keys, to protect the private data. In this paper, based on Chen's multi-key FHE scheme, we first propose secure squared euclidean, comparison, minimum, and average protocols. Then, we design the basic and advanced schemes for implementing the secure multi-party k-means clustering algorithm. In the basic scheme, the implementation of homomorphic multiplication includes the process of transforming ciphertexts under different keys. In order to implement homomorphic multiplication efficiently, the advanced scheme uses an improved method to transform ciphertexts. Meanwhile, almost all computations are completely outsourced to cloud servers. We prove that the proposed protocols and schemes are secure and feasible. Simulation results also show that our improved method is helpful for improving the homomorphic multiplication of Chen's multi-key FHE scheme.

关键词： clustering algorithms Protocols Servers Homomorphic encryption Medical diagnostic imaging Differential privacy Euclidean distance K-means multi-key fully homomorphic encryption multi-party outsourced computing privacy preserving

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Partial Multilabel Learning Using Fuzzy Neighborhood-Based Ball clustering and Kernel Extreme Learning Machine

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第7期31卷 2277-2291页

作者： Sun, Lin Wang, Tianxiang Ding, Weiping Xu, Jiucheng Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

Partial multilabel learning (PML) has attracted considerable interest from scholars. Most PML models construct objective functions and optimize target parameters, which add noise to the training process and results in a poor classification effect. In addition, feature correlation is limited to linear transformations while ignoring the complex relationships among features. In this study, we develop a novel PML model with fuzzy neighborhood-based ball clustering and kernel extreme learning machine (KELM). To reduce the interference from noise, ball k-means clustering is introduced to preprocess partial multilabel data and initialize ball clustering. A new ball clustering model based on fuzzy neighborhood is first designed to address partial multilabel systems. The particle-ball fusion strategy is developed to merge the particle balls reasonably, and the fuzzy membership function and label enhancement are studied for the subsequent training process. Then novel KELM with feature transformation matrix of training data is produced to analyze the nonlinear relationships among features. Finally, a nonsmooth convex objective function with the regression model is constructed to analyze the complex nonlinear relationships among features, and the optimal solutions of three objective parameters are solved by accelerated proximal gradient optimization. Experiments on 14 datasets reveal the effectiveness of the developed algorithm.

关键词： clustering algorithms Data models Training data Kernel Classification algorithms Optimization Linear programming Ball clustering extreme learning machine fuzzy membership fuzzy neighborhood partial multilabel learning (PML)

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Dynamic Construction of Outlier Detector Ensembles With Bisecting K-Means clustering

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IEEE ACCESS 2023年 11卷 24431-24447页

作者： Koko, Rasha Ramadan Z. Yassine, Inas A. Wahed, Manal Abdel Madete, June K. Rushdi, Muhammad A. Minist Hlth Sudan Bioinformat Unit Natl Publ Hlth Lab Khartoum Sudan Cairo Univ Dept Biomed Engn & Syst Giza Egypt Kenyatta Univ Dept Elect & Elect Engn Nairobi Kenya New Giza Univ Sch Informat Technol Giza 12256 Egypt

Outlier detection (OD) is a key problem, for which numerous solutions have been proposed. To deal with the difficulties associated with outlier detection across various domains and data characteristics, ensembles of outlier detectors have recently been employed to improve the performance of individual outlier detectors. In this paper, we follow an ensemble outlier detection approach in which good outlier detectors are selected through an enhanced clustering-based dynamic selection (CBDS) method. In this method, a bisecting K-means clustering algorithm is employed to partition the input data into clusters where every cluster defines a local region of competence. Among the initial pool of detectors, the outputs of the detectors with the most competent local performance were combined through four possible schemes to produce the final OD results. Experimental evaluation and comparison of our method were carried out against four variants of locally selective combination in parallel (LSCP) outlier ensembles. The CBDS-based schemes compare well with the LSCP-based ones on 16 public benchmark datasets and incur considerably lower computational costs. The CBDS method consistently achieved superior average scores of the area under the curve (AUC) of the receiver operating characteristic (ROC), and particularly outperformed the LSCP method on nine of the 16 datasets in terms of the AUC score. In addition, while the CBDS and LSCP methods have similar computational costs on small datasets, the CBDS method achieves significant time savings compared with the LSCP method on large datasets.

关键词： Detectors Anomaly detection clustering algorithms Training Data models Computational efficiency Diversity reception Bisecting K-means dynamic detector selection outlier detection outlier ensemble

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