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Fast Markov clustering Algorithm Based on Belief Dynamics

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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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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OPTIMIZATION OF COLUMNAR NOSQL DATA WAREHOUSE MODEL WITH CLARANS clustering ALGORITHM

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COMPUTING AND INFORMATICS 2023年第3期42卷 762-780页

作者： Soussi, Nassima Sultan Moulay Slimane Univ LIPIM Natl Sch Appl Sci Khouribga Morocco

In order to perfectly meet the needs of business leaders, decision-makers have resorted to the integration of external sources (such as Linked Open Data) in the decision-making system in order to enrich their existing data warehouses with new concepts contributing to bring added value to their organizations, enhance its productivity and retain its customers. However, the traditional data warehouse environment is not suitable to support external Big Data. To deal with this new challenge, several researches are oriented towards the direct conversion of classical relational data warehouse to a columnar NoSQL data warehouse, whereas the existing advanced works based on clustering algorithms are very limited and have several shortcomings. In this context, our paper proposes a new solution that conceives an optimized columnar data warehouse based on CLARANS clustering algorithm that has proven its effectiveness in generating optimal column families. Experimental results improve the validity of our system by performing a detailed comparative study between the existing advanced approaches and our proposed optimized method.

关键词： Big Data columnar NoSQL data warehouse linked open data clustering algorithms Clarans

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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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Harris Hawks Optimization-Based clustering Algorithm for Vehicular Ad-Hoc Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第6期24卷 5822-5841页

作者： Ali, Asad Aadil, Farhan Khan, Muhammad Fahad Maqsood, Muazzam Lim, Sangsoon Univ Engn & Technol Peshawar Dept Comp Sci & Informat Technol Peshawar 25000 Pakistan COMSATS Univ Islamabad Comp Sci Dept Attock Campus Attock 43600 Pakistan Sungkyul Univ Dept Comp Engn Anyang 14097 South Korea

Vehicular ad-hoc network (VANET) is highly dynamic due to the high speed and sparse distribution of vehicles on the road. This creates major challenges (e.g., network fragmentation, packet routing) for the researchers to enable robust, reliable, and scalable communication, especially in a highly dense network. clustering in VANET is one of the remedies to address the scalability issue. However, it is observed in the literature, that existing clustering techniques produce a high number of clusters for the vehicular environment. Consequently, it increases the consumption of scarce resources in a wireless network. Furthermore, it also increases the communication overhead as well as the number of hops for data routing. As a result communication latency also increases and the reliability of communication protocol decreases. So it is highly desirable to find out the optimal clusters for a given vehicular environment. As finding optimal clusters is a multi-objective combinatorial optimization problem, therefore by employing nature-inspired meta-heuristic algorithms we can optimize the multi-objective problem. To this end, we proposed a novel clustering algorithm based on the Harris Hawks Optimization (HHO) algorithm for VANET (HHOCNET). HHO algorithm is a nature-inspired meta-heuristic algorithm inspired by the foraging maneuver of hawks called surprise pounce. The proposed framework imitates the cooperative foraging maneuver of hawks (i.e., surprise pounce for creating optimized vehicular clusters). The stochastic operators of the HHO algorithm and proper maintenance of the equilibrium state between the operations of exploration and exploitation enable the proposed algorithm to escape from the local optima and provide a globally optimal solution (i.e., the optimal number of vehicular clusters). Simulations are performed in MATLAB and the results are compared with the state-of-art schemes (i.e., Gray Wolf optimization-based clustering algorithm (GWOCNET), Multi-objective P

关键词： clustering algorithms Vehicular ad hoc networks Metaheuristics Ad hoc networks Reliability Wireless communication Safety Intelligent clustering VANET clustering intelligent transportation System cluster optimization

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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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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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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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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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