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clustering into Three Groups on a Quantum Processor of Five Spins S=1, Controlled by Pulses of Resonant RF Fields

APPLIED MAGNETIC RESONANCE 2023年第7期54卷 661-677页

作者： Pichkovskiy, I. S. Zobov, V. E. Fed Res Ctr KSC SB RAS Kirensky Inst Phys Krasnoyarsk Russia

We consider a quantum processor based on five qutrits represented by spins S = 1, which is driven by radio frequency (RF) pulses selective in transitions between adjacent levels. Numerical simulation of the implementation of the quantum-adiabatic clustering algorithm was performed on the example of partitioning a set of six points into three groups. We find the amplitudes and durations of rectangular RF pulses, as well as the durations of free evolution intervals in the control pulse sequence, which made it possible to engineer a time-dependent effective Hamiltonian in the discrete-time approximation. Also we studied the dependence of the implementation fidelity on the parameters. We took quadrupole nuclei as qutrits, but the results obtained will be useful for controlling quantum processors based on qutrits represented by other systems.

关键词： clustering algorithms

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Late Fusion Multiple Kernel clustering With Proxy Graph Refinement

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 4359-4370页

作者： Wang, Siwei Liu, Xinwang Liu, Li Zhou, Sihang Zhu, En Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 90014 Finland Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to improve clustering performance. Among existing MKC algorithms, the recently proposed late fusion MKC methods demonstrate promising clustering performance in various applications and enjoy considerable computational acceleration. However, we observe that the kernel partition learning and late fusion processes are separated from each other in the existing mechanism, which may lead to suboptimal solutions and adversely affect the clustering performance. In this article, we propose a novel late fusion multiple kernel clustering with proxy graph refinement (LFMKC-PGR) framework to address these issues. First, we theoretically revisit the connection between late fusion kernel base partition and traditional spectral embedding. Based on this observation, we construct a proxy self-expressive graph from kernel base partitions. The proxy graph in return refines the individual kernel partitions and also captures partition relations in graph structure rather than simple linear transformation. We also provide theoretical connections and considerations between the proposed framework and the multiple kernel subspace clustering. An alternate algorithm with proved convergence is then developed to solve the resultant optimization problem. After that, extensive experiments are conducted on 12 multi-kernel benchmark datasets, and the results demonstrate the effectiveness of our proposed algorithm. The code of the proposed algorithm is publicly available at https://***/wangsiwei2010/graphlatefusion_MKC.

关键词： Kernel clustering algorithms Optimization Partitioning algorithms Task analysis Learning systems Benchmark testing Data fusion multiple kernel clustering (MKC) multi-view learning

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Image Segmentation Based on Fuzzy Low-Rank Structural clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第7期31卷 2153-2166页

作者： Song, Sensen Jia, Zhenhong Yang, Jie Kasabov, Nikola Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Signal Detect & Proc Urumqi 830046 Xinjiang Uygur Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200400 Peoples R China Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland 1020 New Zealand Ulster Univ Data Analyt Derry Londonderry BT48 7JL North Ireland

Fuzzy clustering is an essential algorithm in image segmentation, and most of them are based on fuzzy c-mean algorithms. However, it is sensitive to noise, center point selection, cluster number, and distance metric. To address this problem, we propose a new fuzzy clustering method based on low-rank representation (LRR) for image segmentation, which integrates low-rank structure with fuzzy theory. First, we improve the morphological reconstruction superpixel method based on edge detection by introducing anisotropy to enhance the image edge. Thus, on the one hand, the improved morphological reconstruction superpixel method can improve its noise-resistance performance;on the other hand, the complexity of the subsequent low-rank computation can be reduced by enhancing the superpixels constructed by the edges. Second, inspired by the fact that rank can represent correlation, we propose the concept of fuzzy low-rank structure, which is not dealing with data directly but with the relationship between data. Specifically, we perform rank minimization on the constructed membership matrix to obtain the optimal matrix. To obtain better clustering results, we added the Frobenius norm of the fuzzy matrix as a fuzzy regularization term in the LRR model to achieve global convergence and obtain a membership matrix with a strong element correlation. Finally, we obtain the final clustering results by clustering the processed membership matrix using a subspace clustering with a low-rank structure constraint. Experiments performed on artificial and real-world images show that the proposed method is more effective and efficient than the current state-of-the-art methods.

关键词： clustering algorithms Image segmentation Image edge detection Image reconstruction Minimization Noise measurement Electronic mail Fuzzy clustering fuzzy low-rank structure image segmentation low-rank representation (LRR) superpixel

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clustering of Multilayer Networks Using Joint Learning Algorithm With Orthogonality and Specificity of Features

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第8期53卷 4972-4985页

作者： Wu, Wenming Gong, Maoguo Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Shaanxi Peoples R China

Complex systems in nature and society consist of various types of interactions, where each type of interaction belongs to a layer, resulting in the so-called multilayer networks. Identifying specific modules for each layer is of great significance for revealing the structure-function relations in multilayer networks. However, the available approaches are criticized undesirable because they fail to explicitly the specificity of modules, and balance the specificity and connectivity of modules. To overcome these drawbacks, we propose an accurate and flexible algorithm by joint learning matrix factorization and sparse representation (jMFSR) for specific modules in multilayer networks, where matrix factorization extracts features of vertices and sparse representation discovers specific modules. To exploit the discriminative latent features of vertices in multilayer networks, jMFSR incorporates linear discriminant analysis (LDA) into non-negative matrix factorization (NMF) to learn features of vertices that distinguish the categories. To explicitly measure the specificity of features, jMFSR decomposes features of vertices into common and specific parts, thereby enhancing the quality of features. Then, jMFSR jointly learns feature extraction, common-specific feature factorization, and clustering of multilayer networks. The experiments on 11 datasets indicate that jMFSR significantly outperforms state-of-the-art baselines in terms of various measurements.

关键词： Nonhomogeneous media Feature extraction clustering algorithms Sparse matrices Transforms Social networking (online) Network analyzers Joint learning layer-specific modules multilayer networks non-negative matrix factorization (NMF) sparse representation (SR)

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A Differentiable Perspective for Multi-View Spectral clustering With Flexible Extension

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第6期45卷 7087-7098页

作者： Lu, Zhoumin Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence Key Lab Intelligent Interact & ApplicatOPt & Elec Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Key Lab Intelligent Interact & Applicat OPt & Elect iOPENMinist Ind & Informat Technol Xian 710072 Peoples R China

Multi-view clustering aims to discover common patterns from multi-source data, whose generality is remarkable. Compared with traditional methods, deep learning methods are data-driven and have a larger search space for solutions, which may find a better solution to the problem. In addition, more considerations can be introduced by loss functions, so deep models are highly reusable. However, compared with deep learning methods, traditional methods have better interpretability, whose optimization is relatively stable. In this paper, we propose a multi-view spectral clustering model, combining the advantages of traditional methods and deep learning methods. Specifically, we start with the objective function of traditional spectral clustering, perform multi-view extension, and then obtain the traditional optimization process. By partially parameterizing this process, we further design corresponding differentiable modules, and finally construct a complete network structure. The model is interpretable and extensible to a certain extent. Experiments show that the model performs better than other multi-view clustering algorithms, and its semi-supervised classification extension also has excellent performance compared to other algorithms. Further experiments also show the stability and fewer iterations of the model training.

关键词： Deep learning Kernel clustering algorithms Optimization Neural networks Training Visualization Multi-view learning spectral clustering semi-supervised classification flexible extension differentiable programming

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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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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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