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FCAN-MOPSO: An Improved Fuzzy-Based Graph clustering Algorithm for Complex Networks With Multiobjective Particle Swarm Optimization

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第10期31卷 3470-3484页

作者： Hu, Lun Yang, Yue Tang, Zehai He, Yizhou Luo, Xin Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830000 Peoples R China Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

Performing an accurate clustering analysis is of great significance for us to understand the behavior of complex networks, and a variety of graph clustering algorithms have, thus, been proposed to do so by taking into account network topology and node attributes. Among them, fuzzy clustering algorithm for complex networks (FCAN) is an established fuzzy clustering algorithm that optimizes the memberships of nodes based on dense structures and content relevance. This article proposes an improved fuzzy-based graph clustering algorithm, namely FCAN-multi objective particle swarm optimization (MOPSO) that retains all the benefits associated with FCAN while achieving significantly increased convergence rate using multiobjective particle swarm optimization (MOPSO). To do so, FCAN-MOPSO first modifies the original optimization model of FCAN by adopting an instance-frequency-weighted regularization, which enhances the ability of FCAN-MOPSO to handle the imbalance observed in the distribution of fuzzy memberships of nodes. After that, FCAN-MOPSO decomposes its optimization problem into a set of suboptimization problems. Following the MOPSO framework, FCAN-MOPSO develops an effective solution to reach a consensus optimization among them by balancing the global exploration and local exploitation abilities of particles. A theoretical analysis is provided to prove the global convergence of FCAN-MOPSO. Extensive experiments have been conducted to evaluate the performance of FCAN-MOPSO on five real-world complex networks with different scale, and experimental results demonstrate that when compared with state-of-the-art clustering algorithms, FCAN-MOPSO achieves a better accuracy performance with improved convergence. Hence, FCAN-MOPSO is a promising graph clustering algorithm to precisely and efficiently discover clusters in complex networks.

关键词： clustering algorithms Optimization Complex networks Convergence Particle swarm optimization Heuristic algorithms Task analysis Big data complex network fuzzy graph clustering fuzzy memberships graph representation multiobjective particle swarm optimization (MOPSO)

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Viewpoint-Based Kernel Fuzzy clustering With Weight Information Granules

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第2期7卷 342-356页

作者： Tang, Yiming Pan, Zhifu Pedrycz, Witold Song, Xiaocheng Ren, Fuji Hefei Univ Technol Sch Comp & Informat Anhui Prov Key Lab Affect Comp & Adv Intell Hefei 230601 Anhui Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Hefei Univ Technol Sch Comp & Informat Hefei 230601 Anhui Peoples R China Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610056 Sichuan Peoples R China

Domain knowledge can be introduced into fuzzy clustering with the aid of information granules, embodied by the concept of viewpoints. For such kind of fuzzy clustering methods, the strategy of acquisition of viewpoints has not been fully developed. Furthermore a way of determining the related information granules deserves more attention. Having these problems in mind, in this study, the density Viewpoint-based Weighted Kernel Fuzzy clustering (VWKFC) algorithm is proposed. First, the kernel-based hypersphere density initialization (KHDI) algorithm is presented as a certain prerequisite, in which the kernel distance is utilized instead of the Euclidean distance. Besides, a novel density radius is put forward. Second, the concept of the weight information granule is established, which incorporates two parts. The feature weight matrix is provided, where different weights are assigned to different features to reduce the influence of unrelated features. Meanwhile a sample weight is assigned to each data point, thus the influence of noise and outliers on clustering can be reduced to a certain extent. Third, the data point with the highest local density obtained by KHDI is regarded as the density viewpoint. Then we combine kernel mechanism, density viewpoints, weight information granules and a maximum entropy regularization to design the VWKFC algorithm, and prove its convergence. Experimental results validate that VWKFC is superior over eight related clustering algorithms with regard to five evaluation indexes, especially when processing high-dimensional data. It has been shown that VWKFC makes the selection of initialized cluster centers and viewpoints more reasonable, and obtains better clustering results, and achieves higher convergence speed.

关键词： clustering algorithms Kernel Euclidean distance Computational intelligence Sensitivity Resists Phase change materials Feature weighting fuzzy c-means fuzzy clustering granular computing information granules

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Scalable and Fast Algorithm for Constructing Phylogenetic Trees With Application to IoT Malware clustering

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IEEE ACCESS 2023年 11卷 8240-8253页

作者： He, Tianxiang Han, Chansu Isawa, Ryoichi Takahashi, Takeshi Kijima, Shuji Takeuchi, Jun'ichi Kyushu Univ Grad Sch Fukuoka 8190395 Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan Natl Inst Informat & Commun Technol Koganei 1848795 Japan

With the development of IoT devices, there is a rapid increase in new types of IoT malware and variants, causing social problems. The malware's phylogenetic tree has been used in many studies for malware clustering or better understanding of malware evolution. However, when dealing with a large-scale malware set, conventional methods for constructing a phylogenetic tree is very time-consuming or even cannot be done in a realistic time. To solve this problem, we propose a high-speed, scalable phylogenetic tree construction algorithm with a clustering algorithm to cluster it. The proposed method involves the following steps: (1) Calculating the similarity of the specimen pairs using the normalized compression distance. (2) Creating a phylogenetic tree containing all specimens, instead of calculating the similarity of all pairs of a specimen, our algorithm only calculates a small part of the similarity matrix. (3) Dividing the phylogenetic tree into clusters by applying the minimum description length criterion. In addition, we propose a new online processing algorithm to add new malware specimens into the existing phylogenetic tree sequentially. Our goal is to reduce the computational cost of constructing the phylogenetic tree and improve the clustering accuracy of our previous research. We evaluated our method's clustering accuracy and scalability with 65,494 IoT malware specimens. The results showed that our algorithm reduced the computation by 97.52% compared with the conventional method. Our clustering algorithm achieved accuracies of 95.5% and 99.3% for clustering family name and architecture name, respectively.

关键词： Malware Phylogeny clustering algorithms Static analysis Feature extraction Computational efficiency clustering methods clustering IoT malware MDL criterion phylogenetic tree

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Partitioning Graph clustering With User-Specified Density

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IEEE ACCESS 2023年 11卷 122273-122294页

作者： Tariq, Rohi Lavangnananda, Kittichai Bouvry, Pascal Mongkolnam, Pornchai King Mongkuts Univ Technol Thonburi Sch Informat Technol Bangkok 10140 Thailand Univ Luxembourg Fac Sci Technol & Med Dept Comp Sci L-4365 Esch Sur Alzette Luxembourg

Graph clustering has attracted many interests in recent years, with numerous applications ranging from the clustering of computer networks to the detection of social communities. It presents a challenging NP-class problem, and as a result, numerous algorithms have been developed, each tailored to specific objectives and quality metrics for evaluation. This research commences by categorizing existing graph clustering algorithms based on two distinct perspectives: parameter-free algorithms and user-defined or adjustable parametric algorithms. Quality metrics are further categorized into three distinct groups: internal connectivity, external connectivity, and a combination of both. If a task can be represented by a simple undirected and unweighted graph, from a management and deployment of resources perspective, having clusters of some kind of similar density is advantageous as it allows efficient management. This research introduces a partitioning graph clustering algorithm that allows users to specify the desired density of a cluster by means of 'relative density'. clustering process involves the determination of all triangles (i.e., smallest cliques) and selecting a clique as an initial cluster. The expansion of a cluster is done by adding adjacent cliques while the required relative density is monitored. Existing metrics are found unsuitable for evaluating the proposed method;therefore, a suitable new metric, the Mean Relative Density Deviation Coefficient (MRDDC), is introduced.

关键词： clustering algorithms Measurement Partitioning algorithms Tuning Taxonomy Task analysis Monitoring Quality assurance Graph clustering mean relative density deviation coefficient (MDRCC) NP problem partitioning graph clustering quality metric relative density

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Late Fusion Multiple Kernel clustering With Local Kernel Alignment Maximization

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 993-1007页

作者： Zhang, Tiejian Liu, Xinwang Gong, Lei Wang, Siwei Niu, Xin Shen, Li Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

Multi-view clustering, which appropriately integrates information from multiple sources to reveal data's inherent structure, is gaining traction in clustering. Though existing procedures have yielded satisfactory results, we observe that they have neglected the inherent local structure in the base kernels. This may cause adverse effects on clustering. To solve the problem, we introduce LF-MKC-LKA, a simple yet effective late fusion multiple kernel clustering with local kernel alignment maximisation approach. In particular, we first determine the nearest $k$ neighbours in the average kernel space for each sample and record the information in the nearest neighbor indicator matrix. Then, the nearest neighbor indicator matrix can be used to generate local structure matrix of each sample. The local kernels of each view may then be generated using the local structure matrix, retaining just the highly confident local similarities for learning the intrinsic global manifold of data. They can also be utilised to keep the block diagonal structure and improve the robustness of the underlying kernels against *** input the local kernels of each view into the kernel $k$-means (KKM) algorithm and get the local base partitions. Finally, we use a three-step iterative optimization approach to maximize the alignment of the consensus partition using base partitions and a regularisation term. As demonstrated, a significant number of trials on 11 multi-kernel benchmark datasets have shown that the proposed LF-MKC-LKA is effective and efficient. A number of experiments are also designed to demonstrate the fast convergence, excellent performance, robustness and low parameter sensitivity of the algorithm. Our code can be find at https://***/TiejianZhang/TMM21-LF-MKC-LKA.

关键词： Kernel clustering algorithms Robustness Partitioning algorithms Optimization Manifolds Benchmark testing Multiple kernel clustering neighbor local kernel local base partition block diagonal structure

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clustering Hidden Markov Models With Variational Bayesian Hierarchical EM

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第3期34卷 1537-1551页

作者： Lan, Hui Liu, Ziquan Hsiao, Janet H. Yu, Dan Chan, Antoni B. Beijing Univ Technol Sch Stat & Data Sci Fac Sci Beijing 100124 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Hong Kong Dept Psychol Hong Kong Peoples R China Chinese Acad Sci Acad Math & Syst Sci Inst Syst Sci Beijing 100190 Peoples R China

The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters (K) and the number of hidden states (S) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers K and S are automatically determined in two ways. First, we place a prior on the pair (K,S) and approximate their posterior probabilities, from which the values with the maximum posterior are selected. Second, some clusters and states are pruned out implicitly when no data samples are assigned to them, thereby leading to automatic selection of the model complexity. Experiments on synthetic and real data demonstrate that our algorithm performs better than using model selection techniques with maximum likelihood estimation.

关键词： Hidden Markov models Bayes methods Data models Computational modeling Mixture models clustering algorithms Analytical models clustering hidden Markov mixture model (H3M) hierarchical EM variational Bayesian (VB)

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Workflow for mitigating shape diversity in free-form surface panels through clustering and evaluation

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2025年

作者： Liu, Hequn Liu, Yan Southeast Univ Sch Architecture 2 Sipailou Nanjing 210018 Peoples R China Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

With the advancement of digital technologies, free-form surfaces are increasingly utilized in architectural design. Constructing such surfaces typically involves dividing them into numerous panels, which are often uniquely shaped, thereby significantly increasing fabrication and construction complexity, as well as adding to the economic burden. This paper introduces a workflow for optimizing free-form surface facades by reducing panel shape diversity. The proposed workflow consists of four key components: similarity measures (Euclidean and Manhattan distances), clustering algorithms (hierarchical clustering, K-means, and HDBSCAN), evaluation metrics (SC, DB, and CH), and validation through three case studies. The study confirms the correlation between clustering algorithm performance and surface type, highlighting its dependence on input data characteristics. The workflow is applied to the panel optimization of the Hongdao Railway Station project, validating its practical applicability by focusing on the selection of optimal algorithms and the configuration of clustering parameters. As a modular approach, it establishes a foundation for further refinement and extension of methods in future applications.

关键词： clustering algorithms validation metrics shape similarity assessment free-form surface

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Multi-View Discrete clustering: A Concise Model

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 15154-15170页

作者： Qiang, Qianyao Zhang, Bin Wang, Fei Nie, Feiping Xi An Jiao Tong Univ Sch Software Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China

In most existing graph-based multi-view clustering methods, the eigen-decomposition of the graph Laplacian matrix followed by a post-processing step is a standard configuration to obtain the target discrete cluster indicator matrix. However, we can naturally realize that the results obtained by the two-stage process will deviate from that obtained by directly solving the primal clustering problem. In addition, it is essential to properly integrate the information from different views for the enhancement of the performance of multi-view clustering. To this end, we propose a concise model referred to as Multi-view Discrete clustering (MDC), aiming at directly solving the primal problem of multi-view graph clustering. We automatically weigh the view-specific similarity matrix, and the discrete indicator matrix is directly obtained by performing clustering on the aggregated similarity matrix without any post-processing to best serve graph clustering. More importantly, our model does not introduce an additive, nor does it has any hyper-parameters to be tuned. An efficient optimization algorithm is designed to solve the resultant objective problem. Extensive experimental results on both synthetic and real benchmark datasets verify the superiority of the proposed model.

关键词： Laplace equations clustering algorithms Optimization Symmetric matrices Benchmark testing Analytical models Additives Multi-view clustering graph clustering discrete indicator matrix

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Energy Efficient Wireless Crowd Labeling: Joint Annotator clustering and Power Control

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第3期22卷 2022-2035页

作者： Li, Xiaoyang Zhu, Guangxu Shen, Kaiming Han, Kaifeng Huang, Kaibin Gong, Yi Southern Univ Sci & Technol Dept Elect & Elect Engn EEE Shenzhen 518055 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518115 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China China Acad Informat & Commun Technol Beijing 100045 Peoples R China Univ Hong Kong Dept EEE Hong Kong Peoples R China

The unprecedented growth of mobile data traffic has fueled the deployment of artificial intelligence (AI) at the network edge, while distilling the intelligence from raw data by machine learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast via fading channels to different clusters of annotators for repetition labelling to improve the accuracy. Given the desired labelling accuracy, the superposition coding technique together with the repetition labelling scheme give rise to a new tradeoff between radio-and-annotator resource consumption. Building on such tradeoff, the annotator clustering and transmit power control are jointly optimized to maximize the labelling throughput (i.e., the number of labelled objects) or minimize the power consumption, resulting in NP-hard integer programming problems. To solve these problems, the optimal structure of annotator clustering is derived by exploiting the property that the power allocation for multicasting objects tends to compensate for the worst channel among the annotators in each cluster. Based on such structure, the throughput maximization problem can be recognized as a longest-path problem and solved by means of branch-and-bound, while the power minimization problem can be recasted to a shortest-path problem and solved by means of forward dynamic programming. The solution approaches can be further simplified when the channels are symmetric by merging the same nodes and cutting the identical paths in the path graph. In addition, exact polices are derived for the special cases where either the annotators or power are constrained. Last, simulation results are presented to demonstrate the performance of our proposed joint designs.

关键词： Labeling Wireless communication Resource management Throughput Power control clustering algorithms Servers Crowd labelling clustering power control energy efficiency artificial intelligence

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SMMP: A Stable-Membership-Based Auto-Tuning Multi-Peak clustering Algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第5期45卷 6307-6319页

作者： Guan, Junyi Li, Sheng He, Xiongxiong Zhu, Jinhui Chen, Jiajia Si, Peng Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Zhejiang Univ Affiliated Hosp 2 Sch Med Hangzhou 310014 Peoples R China

Since most existing single-prototype clustering algorithms are unsuitable for complex-shaped clusters, many multi-prototype clustering algorithms have been proposed. Nevertheless, the automatic estimation of the number of clusters and the detection of complex shapes are still challenging, and to solve such problems usually relies on user-specified parameters and may be prohibitively time-consuming. Herein, a stable-membership-based auto-tuning multi-peak clustering algorithm (SMMP) is proposed, which can achieve fast, automatic, and effective multi-prototype clustering without iteration. A dynamic association-transfer method is designed to learn the representativeness of points to sub-cluster centers during the generation of sub-clusters by applying the density peak clustering technique. According to the learned representativeness, a border-link-based connectivity measure is used to achieve high-fidelity similarity evaluation of sub-clusters. Meanwhile, based on the assumption that a reasonable clustering should have a relatively stable membership state upon the change of clustering thresholds, SMMP can automatically identify the number of sub-clusters and clusters, respectively. Also, SMMP is designed for large datasets. Experimental results on both synthetic and real datasets demonstrated the effectiveness of SMMP.

关键词： clustering algorithms Shape Density measurement clustering methods Resource management Periodic structures Time complexity clustering density peak arbitrary shape clustering auto-tuning

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