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MGNR: A Multi-Granularity Neighbor Relationship and Its Application in KNN Classification and clustering Methods

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 7956-7972页

作者： Xie, Jiang Xiang, Xuexin Xia, Shuyin Jiang, Lian Wang, Guoyin Gao, Xinbo Chongqing Univ Telecommun & Posts Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

In the real world, data distributions often exhibit multiple granularities. However, the majority of existing neighbor-based machine-learning methods rely on manually setting a single-granularity for neighbor relationships. These methods typically handle each data point using a single-granularity approach, which severely affects their accuracy and efficiency. This paper adopts a dual-pronged approach: it constructs a multi-granularity representation of the data using the granular-ball computing model, thereby boosting the algorithm's time efficiency. It leverages the multi-granularity representation of the data to create tailored, multi-granularity neighborhood relationships for different task scenarios, resulting in improved algorithmic accuracy. The experimental results convincingly demonstrate that the proposed multi-granularity neighbor relationship effectively enhances KNN classification and clustering methods.

关键词： Computational modeling Data models clustering methods Machine learning clustering algorithms Classification algorithms Task analysis clustering granular-ball computing KNN multi-granularity neighbor relationship

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Graph Contrastive Learning for clustering of Multi-Layer Networks

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IEEE TRANSACTIONS ON BIG DATA 2024年第4期10卷 429-441页

作者： Yang, Yifei Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Key Lab Smart Human Comp Interact & Wearable Techn Xian 710071 Peoples R China

Multi-layer networks precisely model complex systems in society and nature with various types of interactions, and identifying conserved modules that are well-connected in all layers is of great significance for revealing their structure-function relationships. Current algorithms are criticized for either ignoring the intrinsic relations among various layers, or failing to learn discriminative features. To attack these limitations, a novel graph contrastive learning framework for clustering of multi-layer networks is proposed by joining nonnegative matrix factorization and graph contrastive learning (called jNMF-GCL), where the intrinsic structure and discriminative of features are simultaneously addressed. Specifically, features of vertices are first learned by preserving the conserved structure in multi-layer networks with matrix factorization, and then jNMF-GCL learns an affinity structure of vertices by manipulating features of various layers. To enhance quality of features, contrastive learning is executed by selecting the positive and negative samples from the constructed affinity graph, which significantly improves discriminative of features. Finally, jNMF-GCL incorporates feature learning, construction of affinity graph, contrastive learning and clustering into an overall objective, where global and local structural information are seamlessly fused, providing a more effective way to describe structure of multi-layer networks. Extensive experiments conducted on both artificial and real-world networks have shown the superior performance of jNMF-GCL over state-of-the-art models across various metrics.

关键词： clustering algorithms Network analyzers Representation learning Couplings Complex systems Big Data Analytical models Multi-layer networks graph clustering nonnegative matrix factorization graph contrastive learning

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Self-Weighted Multi-View Fuzzy clustering With Multiple Graph Learning

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1585-1589页

作者： Liu, Chaodie Chang, Cheng Nie, Feiping Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Peoples R China Henan Normal Univ Key Lab Artificial Intelligence & Personalized Lea Xinxiang 453007 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect IOPEN Xian 710072 Peoples R China Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China

Graph-based multi-view clustering has garnered considerable attention owing to its effectiveness. Nevertheless, despite the promising performance achieved by previous studies, several limitations remain to be addressed. Most graph-based models employ a two-stage strategy involving relaxation and discretization to derive clustering results, which may lead to deviation from the original problem. Moreover, graph-based methods do not adequately address the challenges of overlapping clusters or ambiguous cluster membership. Additionally, assigning appropriate weights based on the importance of each view is crucial. To address these problems, we propose a self-weighted multi-view fuzzy clustering algorithm that incorporates multiple graph learning. Specifically, we automatically allocate weights corresponding to each view to construct a fused similarity graph matrix. Subsequently, we approximate it as the scaled product of fuzzy membership matrices to directly derive clustering assignments. An iterative optimization algorithm is designed for solving the proposed model. Experiment evaluations conducted on benchmark datasets illustrate that the proposed method outperforms several leading multi-view clustering approaches.

关键词： clustering algorithms Data mining Benchmark testing Linear programming Computational complexity Training Signal processing algorithms Optimization Flowering plants Vectors Fuzzy clustering graph learning multi-view clustering self-weighted

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A Distributed Higher-Order k-Medoids clustering Algorithm for Network Partition

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4181-4193页

作者： Mo, Yuanqiu Xing, Ran Hou, Huazhou Southeast Univ Nanjing 211189 Peoples R China Univ Sydney Sydney NSW 2006 Australia Purple Mt Labs Nanjing 211102 Peoples R China

In this paper, we develop a distributed higher-order k-medoids clustering algorithm for networks using hop count as the distance metric, typical examples include social networks, wireless sensor networks and etc. Different than the classical k-medoids clustering where each cluster is assigned with one medoid (representative nodes in the network), the proposed algorithm is capable of partitioning the nodes into clusters dominated by multiple medoids. In this algorithm, a higher-order shortest path algorithm is first adopted to assign each node the clustering set comprising of its h (h >= 1) closest medoids using the hop count metric, then an aggregation algorithm collects the local information from each node to the medoids in its clustering set, based on which a medoids update mechanism helps each medoid decide whether to keep its medoid status or assign the status to one of its neighbors, so as to improve the clustering quality. The clustering performance of the proposed algorithm is proved to monotonically converge to a local optimum after running the algorithm finite times. Simulations results show that the proposed algorithm can achieve superior performance compared with several centralized $k$-medoids clustering algorithms and scale well as the number of nodes increases.

关键词： clustering algorithms Partitioning algorithms Length measurement Wireless sensor networks Social networking (online) Search problems Prototypes k -medoids clustering higher-order clustering graph algorithms

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Prototype Imputation Guided Incomplete Multi-View clustering

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1565-1569页

作者： Hong, Rui Chen, Xiao-Ping Zhou, Yan Liu, Hui Wan, Tiancai Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Avic Chengdu Aircraft Design & Res Inst Chengdu 610091 Peoples R China

In real-world data collection, certain views may become partially unavailable due to sensor malfunctions or equipment faults, giving rise to the challenge of incomplete multi-view clustering (IMVC). Although some IMVC methods that utilize prototype completion yield acceptable performance, the majority implicitly assume the correct alignment of prototypes across all views. However, the generated prototypes could have the potential misalignment of prototypes across views. To tackle this issue, we propose a new Prototype Imputation Guided Incomplete Multi-view clustering (PIGIMC), which can reduce the impact of prototype mismatch. Specifically, RPCIC first employs a multi-view contrastive learning module to obtain consistent feature representations across various views. To mitigate the adverse impact caused by multi-view prototype mismatch, we propose a prototype matching learning to achieve prototype alignment. Lastly, we propose a prototype imputation strategy to impute the missing data, thereby achieving IMVC. Extensive experiments confirm that PIGIMC surpasses 11 state-of-the-art IMVC methods.

关键词： Prototypes Imputation Decoding Contrastive learning Training Data mining Machine learning Interference clustering algorithms Bipartite graph Incomplete multi-view clustering prototype alignment prototype matching learning

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Energy-Balanced Routing Protocol With Nonuniform clustering for Underwater Acoustic Sensors Networks

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IEEE SENSORS JOURNAL 2024年第22期24卷 38082-38091页

作者： Jin, Zhigang Li, Haoyong Wang, Ying Liang, Jiawei Cheng, Simeng Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Energy consumption has been the focus of routing protocols in underwater acoustic sensor networks (UASNs), and many cluster-based routing protocols have been proposed to optimize energy consumption. However, there is the "hotspot" problem resulting from frequent data forwarding by cluster heads (CHs) and energy inefficiency caused by the transmission of data packets from shallow water to deep water. Therefore, we propose an energy-balanced routing protocol with nonuniform clustering (ERNC) to balance energy consumption and improve data transmission efficiency. First, without accurate 3-D localization, nodes exchange information with each other, and the combined coordinate of layer ID and hop ID is proposed to represent the node's location information for subsequent CH selection and intercluster routing. Then, the combined coordinate, residual energy, and node density are considered comprehensively to select CHs for making their distribution uneven and equalizing the energy consumption. In the intercluster routing phase, the next hop candidate node sets with different forwarding priorities are constructed based on nodes' coordinates to improve the network transmission efficiency. Moreover, we design the different holding times for the next-hop nodes in the same set to balance the energy consumption of CHs. The simulation results show that ERNC can effectively extend the network lifetime and improve the data transmission performance.

关键词： Energy consumption Routing protocols clustering algorithms Routing Data communication Three-dimensional displays Optimization Underwater acoustics Particle swarm optimization Monitoring Energy consumption balance nonuniform clustering routing protocol underwater acoustic sensor networks (UASNs)

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Design and Hardware Implementation of Low-Latency 4-Splitting Tree-Structure-Based K-Means clustering Trainer and Classifier Chip for Detecting Cybersecurity Attacks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第4期72卷 613-617页

作者： Shih, Xin-Yu Chen, Hsi-Cheng Hung, Xin-Liang MediaTek Dept Wireless Connect Network Hsinchu 300 Taiwan Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 804 Taiwan

In this brief, we propose a first system chip architecture of 4-splitting tree-structure-based K-means clustering trainer and classifier. It mainly aims to efficiently detect cybersecurity attacks in the emerging AI-driven world. In addition, we also propose three design techniques to elaborate our chip, featuring a low-latency property in both training and classification aspects. Our developed architecture is first validated with the Xilinx FPGA platform. Furthermore, it is implemented in ASIC and well-verified with five representative cybersecurity-related datasets. The total core area with TSMC 40nm low-Vt CMOS technology only occupies 0.44 um(2), maximally operating at 434.78 MHz and possessing a maximum classification throughput of 3.48 GBps on chip. In comparison, the speed-up ratio of training and classification is up to 9.33 and 8.73 times, respectively, successfully delivering a low-latency characteristic.

关键词： Training Training data Hardware Time division multiplexing Low latency communication Accuracy Machine learning Data mining clustering algorithms Semisupervised learning Tree structure K-means low latency chip architecture cybersecurity detection machine learning semi-supervised learning

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Fuzzy clustering From Subset-clustering to Fullset-Membership

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第9期32卷 5359-5370页

作者： Chen, Huimin Duan, Yu Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Fuzzy theory, which extends precise binary logic to continuous fuzzy logic, provides an effective tool for uncertainty problems in machine learning and thus, evolved fuzzy methods such as fuzzy c-means and fuzzy graph clustering. Among them, graph-based clustering methods have become a hot spot in the field of unsupervised clustering due to their good ability to process nonlinear data. Unfortunately, they usually suffer from high time complexity and cumbersome regularization parameter tuning, so their practical applications are greatly limited. To this end, we propose an efficient graph-cut algorithm called fS2F. Based on the similarity graph between the dataset and landmark subset, fS2F transforms the membership learning of the entire dataset into a clustering problem of representative points, which greatly improves its clustering efficiency. In addition, fS2F softly constrains the cluster size in a way that does not require additional regularization parameters so that it can be widely and conveniently applied. The article also presents the optimization method for this model and demonstrates its effectiveness through experiments.

关键词： clustering algorithms clustering methods Task analysis Eigenvalues and eigenfunctions Tuning Transforms Fuzzy systems Fuzzy clustering graph cut label transmission parameter-free

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DGBPSO-DBSCAN: An Optimized clustering Technique Based on Supervised/Unsupervised Text Representation

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IEEE ACCESS 2024年 12卷 110798-110812页

作者： Abdulsahib, Asma Khazaal Balafar, M. A. Baradarani, Aryaz Univ Tabriz Fac Elect & Comp Engn Tabriz *** East Azerbaijan Iran Tessonics Inc Ctr Diagnost Imaging Res Windsor ON N9A Canada

Density-based spatial clustering of noisy applications (DBSCAN), a widely used density-based clustering technique, faces challenges in determining its key parameter, Eps, leading to manual specification and suboptimal clustering outcomes. Additionally, its time complexity poses limitations. This study introduces a novel approach to enhance the algorithm's performance. We combined the DBSCAN algorithm with another approach, the particle swarm optimization, based on a novel way to represent text, termed a dependency graph;this method is recognized as DGBPSO-DBSCAN (Dependency Graph Based Particle swarm algorithm for DBSCAN). This paper focuses on employing a novel approach for PSO variable upgrading to explore the Eps range more rapidly and effectively. This method offers a selection of informative elements from the text, where the initial groups are derived from the graph-based degree centrality, the PSO method is used to choose the most compelling features, and the DBSCAN algorithm is used for text clustering. The experimental findings indicate that the modified PSO is used to improve DBSCAN. We compared the outcomes of the suggested technique to those of the standard clustering algorithm. According to the assessment criteria MSE, accuracy, precision, recall, and F-measure, our technique has been demonstrated to be superior to the conventional method.

关键词： clustering algorithms Reviews Particle swarm optimization Accuracy Partitioning algorithms Optimization Noise measurement Decision trees DBSCAN algorithm decision tree degree centrality dependency graph

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High-order Topology for Deep Single-Cell Multiview Fuzzy clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第8期32卷 4448-4459页

作者： Hu, Dayu Dong, Zhibin Liang, Ke Yu, Hao Wang, Siwei Liu, Xinwang Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Intelligent Game & Decis Lab Beijing 100071 Peoples R China

Single-cell multiview clustering is essential for analyzing the different cell subtypes of the same cell from different views. Some attempts have been made, but most of these models still struggle to handle single-cell sequencing data, primarily due to their nonspecific design for cellular data. We observe that such data distinctively exhibits: 1) a profusion of high-order topological correlations, 2) a disparate distribution of information across different views, and 3) inherent fuzzy characteristics, indicating a cell's potential to associate with multiple cluster identities. Neglecting these key cellular patterns could significantly impair medical clustering. In response, we propose a specialized application of fuzzy clustering for single-cell sequencing data, namely, the deep single-cell multiview fuzzy clustering method. Concretely, we employ a random walk technique to capture high-order topological relationships on the cell graph and have developed a cross-view information aggregation mechanism that adaptively assigns weights to different views. Furthermore, to accurately reflect the dynamic insight in cellular development, we propose a deep fuzzy clustering strategy that allows cells to associate with diverse clusters. Extensive experiments conducted on three real-world single-cell multiview datasets demonstrate our method's superior performance.

关键词： Sequential analysis clustering methods clustering algorithms Fuzzy systems Partitioning algorithms Data models Topology Cross-view aggregation deep fuzzy clustering high-order topology multiview clustering

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