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A Metaheuristic Search Algorithm Based on Sampling and clustering

IEEE ACCESS 2024年 12卷 15493-15508页

作者： Harita, Maria Wong, Alvaro Suppi, Remo Rexachs, Dolores Luque, Emilio Univ Autonoma Barcelona Dept Comp Architecture & Operating Syst Bellaterra 08193 Spain

As optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-clustering Search Algorithm), which is based on Montecarlo sampling, and a clustering strategy involving two techniques. Our objective is to apply MCSA, an inherently stochastic method, to address optimization problems. To assess its performance, we conducted an evaluation using classical benchmark optimization functions. Additionally, we leveraged the CEC2017 benchmark suite to comprehensively evaluate the algorithm, highlighting the pivotal role of the Exploration stage in our methodology. Subsequently, we extended our methodology to tackle a practical combinatorial problem, the Knapsack problem. This NP-Hard problem holds significant real-world applications in resource allocation, scheduling, planning, logistics, and more. Our contributions lie in parameterizing the Knapsack Problem to align with MCSA's parameters for reference indicators adjustment and achieving high-quality solutions, surpassing 90% in comparison to exhaustive methods such as branch and bound.

关键词： clustering algorithms Optimization Heuristic algorithms Metaheuristics Search problems Benchmark testing Partitioning algorithms Benchmarks heuristic methods Knapsack problem Montecarlo and clustering methods optimization

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Self-Supervised Deep Multiview Spectral clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 4299-4308页

作者： Zong, Linlin Miao, Faqiang Zhang, Xianchao Liang, Wenxin Xu, Bo Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Inst Farmland Irrigat CAAS Xinxiang 453003 Henan Peoples R China Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Multiview spectral clustering has received considerable attention in the past decades and still has great potential due to its unsupervised integration manner. It is well known that pairwise constraints boost the clustering process to a great extent. Nevertheless, the constraints are usually marked by human beings. To ameliorate the performance of multiview spectral clustering and alleviate the consumption of human resources, we propose self-supervised multiview spectral clustering with a small number of automatically retrieved pairwise constraints. First, the fused multiple autoencoders are used to extract the latent consistent feature of multiple views. Second, the pairwise constraints are achieved based on the commonality among multiple views. Then, the pairwise constraints are propagated through the neural network with historical memory. Finally, the propagated constraints are used to optimize the fused affinity matrix of spectral clustering. Our experiments on four benchmark datasets show the effectiveness of our proposed approach.

关键词： Feature extraction clustering algorithms Task analysis Data mining Software Decoding Matrix decomposition Constraint propagation network deep multiview self-supervised spectral clustering

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One-Step Joint Learning of Self-Supervised Spectral clustering With Anchor Graph and Fuzzy clustering for Land Cover Classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 11178-11193页

作者： Wu, Chengmao Zhang, Jiale Xian Univ Posts & Telecommun Xian 710121 Peoples R China

Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms;however, in practice, it is often unable to meet the demand for a priori information;at the same time, the two-stage structure of spectral clustering leads to bias and information loss during its clustering process. To address the above problems, we propose a one-step joint learning of self-supervised spectral clustering with anchor graph and fuzzy clustering. In this algorithm, we first use the BKHK method to generate anchor points that vary with the size of the dataset to enhance the quality of the similarity graph. After that, we use the idea of fuzzy clustering to optimize the membership and simultaneously learn the local and global structures of the data. Moreover, clustering information is integrated with pairwise constraint information in a unified update framework, which achieves mutual learning of clustering results and constraints under soft partitioning. After comparing with the existing algorithms, the proposed algorithm has better performance in classifying numerical datasets, remote sensing images, and hyperspectral datasets.

关键词： clustering algorithms Hyperspectral imaging Partitioning algorithms Classification algorithms Unsupervised learning Training Optimization Anchor graph fuzzy clustering hyperspectral image self-supervised spectral clustering

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One-pass View-unaligned clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 9699-9709页

作者： Dong, Wenhua Wu, Xiao-Jun Feng, Zhenhua Ahmed, Sara Atito Ali Awais, Muhammad Kittler, Josef Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH England Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Univ Surrey Sch Comp Sci & Elect Engn Guildford GU2 7XH England

Given a set of multi-view instances, the prevailing assumption in most existing clustering approaches is that they are complete and exhibit cross-view alignment. However, this assumption is often unrealistic. In such scenarios, it could be satisfied at the cost of data pre-processing, but this would be complex and inconsistent with practical applications. Therefore, developing more effective solutions for the View-unaligned Problem (VuP) is highly desirable. Several pioneering works have tackled the partially VuP, yet handling fully VuP remains a challenge due to the reliance on partially pre-aligned instances. In this paper, we propose One-pass View-unaligned clustering (OpVuC) that simultaneously aligns and clusters instances in a unified framework. Specifically, we align shuffled instances with a selected template using an innovative global-local alignment scheme based on the notion of geometric invariance and separate the fully aligned instances using a relaxed $k$-means algorithm. The proposed OpVuC method can handle VuP at any alignment level without requiring any pre-aligned instances. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness and merits of the proposed OpVuC method.

关键词： clustering methods clustering algorithms Vectors Scalability Noise robustness Task analysis Speech recognition Multi-view clustering view-unaligned problem global-local alignment relaxed k-means

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Strong Consistency of Spectral clustering for the Sparse Degree-Corrected Hypergraph Stochastic Block Model

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第3期70卷 1962-1977页

作者： Deng, Chong Xu, Xin-Jian Ying, Shihui Shanghai Univ Dept Math Shanghai 200444 Peoples R China Shanghai Univ Qianweichang Coll Shanghai 200444 Peoples R China

We prove strong consistency of spectral clustering under the degree-corrected hypergraph stochastic block model in the sparse regime where the maximum expected hyperdegree is as small as Omega (log n) with n denoting the number of nodes. We show that the basic spectral clustering without preprocessing or postprocessing is strongly consistent in an even wider range of the model parameters, in contrast to previous studies that either trim high-degree nodes or perform local refinement. At the heart of our analysis is the entry-wise eigenvector perturbation bound derived by the "leave-one-out"technique. To the best of our knowledge, this is the first entry-wise error bound for degree-corrected hypergraph models, resulting in the strong consistency for clustering non-uniform hypergraphs with heterogeneous hyperdegrees.

关键词： clustering algorithms Linear matrix inequalities Stochastic processes Perturbation methods Numerical models Image edge detection Approximation algorithms Hypergraph stochastic block model spectral clustering consistency

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Temporal and Multivariate Similarity clustering of 5G Performance Data

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IEEE ACCESS 2024年 12卷 114137-114145页

作者： Mazgula, Jakub Krol, Dariusz Jablonski, Ireneusz Wroclaw Univ Sci & Technol Fac Informat & Commun Technol PL-50370 Wroclaw Poland Nok Solut & Networks PL-54130 Wroclaw Poland Brandenburg Tech Univ Cottbus Fac Phys D-03046 Cottbus Germany Fraunhofer Inst Photon Microsyst Branch Integrated Silicon Syst D-03046 Cottbus Germany

The performance of 5G mobile network cells is highly impacted by their evolving configuration and temporal environmental conditions, such as the number of connected devices or resource utilization. Evaluation of the performance of such a system is a complex task that requires the simultaneous analysis of multiple indicators and inspires the research community to work on zero-touch network service management. In this paper, we present a novel time series clustering method - Temporal and Multivariate Similarity clustering (TMSC) - that incorporates Dynamic Time Warping with Limited Warping Length and Spectral clustering, allowing for radio cell grouping based on realization of multiple Key Performance Indicators. We evaluated TMSC against state-of-the-art algorithms at a practical task of identifying cell configuration differences by clustering their performance metrics with a limited set of observations. The proposed algorithm outperformed other methods regarding the Normalized Mutual Information score achieved for more than 95% of the cases studied. We also display the potential for method generalization by evaluating it at the hand gesture recognition task, which yields satisfactory results.

关键词： Time series analysis 5G mobile communication clustering algorithms Matrices Key performance indicator Cellular networks Vectors Spectral analysis Multivariate regression 5G network performance radio cell management spectral clustering dynamic time warping multivariate time series temporal similarity

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FSS-DBSCAN: Outsourced Private Density-Based clustering via Function Secret Sharing

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 7759-7773页

作者： Fu, Jiaxuan Cheng, Ke Song, Anxiao Xia, Yuheng Chang, Zhao Shen, Yulong Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Peoples R China

Density-based clustering algorithms such as DBSCAN, are highly effective in handling large datasets and identifying clusters of arbitrary shapes, playing a crucial role in data analysis fields like outlier detection and social networks. Outsourcing DBSCAN to the cloud brings substantial benefits but also raises major privacy concerns regarding the private input data of data owners. Existing private DBSCAN methods often face challenges of inefficiency or potential privacy leakage, hindering their practical deployment. To address these challenges, we introduce FSS-DBSCAN, a three-server MPC platform designed for outsourced private density-based clustering using function secret sharing (FSS). This solution guarantees clustering quality equivalent to plaintext algorithms, ensures comprehensive privacy protection, and achieves top-tier efficiency. The high performance of FSS-DBSCAN is driven by two pivotal strategies. First, we devise an MPC-friendly DBSCAN algorithm that is highly compatible with efficient secret-sharing-based cryptographic protocols and benefits from GPU acceleration. Second, we construct novel FSS-based protocols tailored for complex operations integral to our DBSCAN variant, such as Euclidean distance comparison and point assignment, and further optimize their computation through tensorization techniques. We implement our platform as an extensible system on top of PyTorch that leverages GPU hardware acceleration for cryptographic and tensorized operations. These innovations enable FSS-DBSCAN to significantly outperform ppDBSCAN (AsiaCCS 2021), reducing the clustering time for 5000 samples to approximately 2 hours, achieving an $83.4\times $ speed improvement.

关键词： clustering algorithms Protocols Cryptography Graphics processing units Additives Outsourcing Distributed databases Secure clustering DBSCAN additive secret sharing secure comparison

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Deep Multirepresentation Learning for Data clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 15675-15686页

作者： Sadeghi, Mohammadreza Armanfard, Narges McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada Mila Quebec AI Inst Montreal PQ H2S 3H1 Canada

Deep clustering incorporates embedding into clustering in order to find a lower-dimensional space suitable for clustering tasks. Conventional deep clustering methods aim to obtain a single global embedding subspace (aka latent space) for all the data clusters. In contrast, in this article, we propose a deep multirepresentation learning (DML) framework for data clustering whereby each difficult-to-cluster data group is associated with its own distinct optimized latent space and all the easy-to-cluster data groups are associated with a general common latent space. Autoencoders (AEs) are employed for generating cluster-specific and general latent spaces. To specialize each AE in its associated data cluster(s), we propose a novel and effective loss function which consists of weighted reconstruction and clustering losses of the data points, where higher weights are assigned to the samples more probable to belong to the corresponding cluster(s). Experimental results on benchmark datasets demonstrate that the proposed DML framework and loss function outperform state-of-the-art clustering approaches. In addition, the results show that the DML method significantly outperforms the SOTA on imbalanced datasets as a result of assigning an individual latent space to the difficult clusters.

关键词： clustering algorithms Training Task analysis Decoding Optimization Learning systems Image reconstruction Autoencoder (AE) cluster-specific AEs data clustering multiple representation learning

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EDMC: Efficient Multi-View clustering via Cluster and Instance Space Learning

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 5273-5283页

作者： Qin, Yalan Pu, Nan Wu, Hanzhou Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Univ Trento Dept Informat Engn & Comp Sci I-40128 Trento Italy

Multi-view subspace clustering aims to cluster the data lying in a union of subspaces with low dimensions. The commonly used spectral clustering performs the final clustering based on an n x n affinity graph, which suffers from relative high time and space complexity. Some existing works have chosen key anchors with uniform sampling strategy or K-means for dealing with large-scale datasets. However, few of them pay attention to the physical meaning of cluster representation in the column of the dataset for learning informative anchors, which is independent from the instance representation. In this paper, we propose efficient dual multi-view clustering (EDMC) with relative low complexity. To be specific, EDMC makes full use of cluster representation space in the column of the dataset to help produce informative anchors, which has a clear physical meaning and is independent of instance representation in the row. It simultaneously explores the cluster and instance subspace representations to learn anchors for large-scale datasets. We perform anchor learning and efficient multi-view clustering in a unified framework and then adopt an alternative optimization strategy for solving the formulated problem. Extensive experiments performed on different datasets in terms of several metrics validate the superiority of the proposed method.

关键词： clustering algorithms Tensors Scalability Representation learning Optimization Dimensionality reduction Complexity theory Multi-view subspace clustering cluster representation instance representation anchor learning a unified framework

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Consensus clustering With Co-Association Matrix Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 4192-4205页

作者： Shi, Yifan Yu, Zhiwen Chen, C. L. Philip Zeng, Huanqiang Huaqiao Univ Sch Engn Quanzhou 362021 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Shenzhen 518066 Peoples R China

Consensus clustering can derive a more promising and robust clustering result by integrating multiple partitions strategically. However, there are several limitations in the existing approaches: 1) most of the methods compute the ensemble-information matrix heuristically and lack of sufficient optimization;2) the information from the original dataset is rarely considered;and 3) the noise in both label space and feature space is ignored. To address these issues, we proposed a novel consensus clustering method with co-association matrix optimization (CC-CMO), which aims at improving the co-association matrix by taking abundant information from both label space and feature space into consideration. In label space, CC-CMO derives a weighted partition matrix capturing the intercluster correlation and further designs a least squares regression (LSR) model to explore the global structure of data. In feature space, CC-CMO minimizes the reconstruction error with doubly stochastic normalization in the projective subspace to eliminate noise features as well as learn the local affinity of data. To improve the co-association matrix by jointly considering the subspace representation, global structure, and local affinity of data, we explicitly propose a unified optimization framework and design an alternating optimization algorithm for the optimal co-association matrix. Extensive experiments on a variety of real-world datasets demonstrate the superior performance of CC-CMO to the state-of-the-art consensus clustering approaches.

关键词： Optimization clustering algorithms Partitioning algorithms Linear programming Correlation clustering methods Stochastic processes Alternative optimization consensus clustering

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