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Multi-View Random-Walk Graph Regularization Low-Rank Representation for Cancer clustering and Differentially Expressed Gene Selection

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第7期26卷 3578-3589页

作者： Wang, Juan Wang, Li-Hong Liu, Jin-Xing Kong, Xiang-Zhen Li, Sheng-Jun Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China

Cancer genome data generally consists of multiple views from different sources. These views provide different levels of information about gene activity, as well as more comprehensive cancer information. The low-rank representation (LRR) method, as a powerful subspace clustering method, has been extended and applied in cancer data research. Although the multi-view learning methods based on low rank representation have achieved good results in cancer multi-omics analysis because they fully consider the consistency and complementarity between views, these methods have some shortcomings in mining the potential local geometry of data. In view of this, this paper proposes a new method named Multi-view Random-walk Graph regularization Low-Rank Representation (MRGLRR) to comprehensively analyze multi-view genomics data. This method uses multi-view model to find the common centroid of view. By constructing a joint affinity matrix to learn the low-rank subspace representation of multiple sets of data, the hidden information of each view is fully obtained. In addition, this method introduces random walk graph regularization constraint to obtain more accurate similarity between samples. Different from the traditional graph regularization constraint, after constructing the KNN graph, we use the random walk algorithm to obtain the weight matrix. The random walk algorithm can retain more local geometric information and better learn the topological structure of the data. What's more, a feature gene selection strategy suitable for multi-view model is proposed to find more differentially expressed genes with research value. Experimental results show that our method is better than other representative methods in terms of clustering and feature gene selection for cancer multi-omics data.

关键词： Cancer Data models Bioinformatics Analytical models Manifolds Sparse matrices clustering algorithms Graph regular constraint low-rank representation multi-view model random walk

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A Novel Minimum Spanning Tree clustering Algorithm Based on Density Core

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022年第0期2022卷 8496265页

作者： Gao, Qiang Gao, Qin-Qin Xiong, Zhong-Yang Zhang, Yu-Fang Zhang, Min Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China

clustering analysis is an unsupervised learning method, which has applications across many fields such as pattern recognition, machine learning, information security, and image segmentation. The density-based method, as one of the various clustering algorithms, has achieved good performance. However, it works poor in dealing with multidensity and complex-shaped datasets. Moreover, the result of this method depends heavily on the parameters we input. Thus, we propose a novel clustering algorithm (called the MST-DC) in this paper, which is based on the density core. Firstly, we employ the reverse nearest neighbors to extract core objects. Secondly, we use the minimum spanning tree algorithm to cluster the core objects. Finally, the remaining objects are assigned to the cluster to which their nearest core object belongs. The experimental results on several synthetic and real-world datasets show the superiority of the MST-DC to Kmeans, DBSCAN, DPC, DCore, SNNDPC, and LDP-MST.

关键词： clustering algorithms

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Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2024年第1期2024卷 6691579-6691579页

作者： Zhou, Xiaoya Zhu, Qi Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing 210000 Peoples R China Nanjing Univ Posts & Telecommun Engn Res Ctr Hlth Serv Syst Based Ubiquitous Wirel Minist Educ Nanjing 210000 Peoples R China

Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%.

关键词： clustering algorithms

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An Intelligent Web Service Discovery Framework Based on Improved Biterm Topic Model

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IEEE ACCESS 2024年 12卷 144437-144455页

作者： Yuan, Yuan Du, Yegang Pan, Jun Zhejiang Univ Sci & Technol Sch Sci Hangzhou 310023 Zhejiang Peoples R China Tohoku Univ Smart Ageing Int Res Ctr Sendai 9808575 Japan

Given the proliferation of Web services, effectively identifying the most suitable ones based on user queries poses a formidable challenge. In response to the challenges posed by the reduced efficiency of service discovery methods as the number of services continues to grow and the limited co-occurrence of word frequencies in service description documents, this study proposes an intelligent service discovery framework based on probabilistic topic distribution. The framework utilizes the Biterm Topic Model to extract the probabilistic topic distribution from both service description documents and user requirements. It then performs functional clustering and service matching based on this probabilistic topic distribution, resulting in a set of candidate services. To expedite the training process of the topic model, a topic model training algorithm employing sampling recombination is introduced, which reorganizes the topic sampling process and reduces training time. Additionally, a functional clustering algorithm based on weighted connected graphs is presented to enhance the quality of clustering. Experimental results validate the effectiveness of the proposed framework, which significantly reduces the training time required for the topic model and service discovery while improving the accuracy of service discovery and the normalized discounted cumulative gain.

关键词： Web services clustering algorithms Semantics Training Probabilistic logic Data models Accuracy Vectors Syntactics Classification algorithms Query processing Service discovery biterm topic model sampling recombination weighted connected graphs

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GFS-Node: Graph Fuzzy Systems for Node Prediction

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第12期32卷 6822-6834页

作者： Hu, Fuping Deng, Zhaohong Xie, Zhenping Zhang, Te Choi, Kup-Sze Zhang, Fan Wang, Shitong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Healthcare Wuxi 214122 Peoples R China Univ Nottingham Sch Comp Sci Lab Uncertainty Data & Decis Making Nottingham NG7 2RD England TechCosmos Ltd Hong Kong Peoples R China Inspur Zhuoshu Big Data Ind Dev Co Ltd Wuxi 214062 Peoples R China

Graph data modeling is nontrivial due to the challenges to ensure model interpretability and handle data uncertainty. While methods derived from deep learning models, such as graph neural networks (GNNs), are able to handle graph data, the interpretability is limited. Graph fuzzy systems (GFSs) based on the fuzzy rules and fuzzy inference have been proposed to improve interpretability, but the existing methods are developed for whole graph prediction only and cannot deal with node prediction, which is a more common task in graph data modeling. To tackle the challenges, a novel GFS for node prediction (GFS-node) is investigated in this study. For this purpose, the concepts, framework, and algorithms of GFS-node are systematically developed. First, several related concepts are defined, including the node fuzzy rule base, node fuzzy set, and node consequent processing module (NCPM). A general framework for GFS-node is then presented, where the construction of antecedents and the consequents of fuzzy rules are analyzed. Furthermore, a concrete implementation method of GFS-node is designed. In particular, the kernel K virtual central nodes clustering (KVCN) algorithm is proposed to develop the algorithm for antecedent generation, and the linear message passing network (LMPN) is adopted to develop the algorithm for consequent generation and learning. Experiments are carried out on multiple benchmark datasets, and the results show that GFS-node combines the advantages of both traditional fuzzy systems and classical GNNs for node prediction.

关键词： Fuzzy systems Data models Predictive models Uncertainty Prediction algorithms Fuzzy sets clustering algorithms Graph fuzzy systems for node modeling (GFS-node) graph learning kernel K virtual central nodes clustering (KVCN) linear message passing network (LMPN) node consequent processing module (NCPM) node fuzzy sets node prediction

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Creation of the Internal Categories for Low-Voltage Three-Phase Electricity Users

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第4期60卷 5423-5435页

作者： Chicco, Gianfranco Bonansinga, Daniele Colella, Pietro Solida, Lorenzo Politecn Torino Dipartimento Energia Galileo Ferraris I-10129 Turin Italy

The categorisation of electricity users is carried out by Distributor System Operators (DSOs) and retailers to create synthetic load profiles. Initially, some macro-categories are defined based on general attributes of the users, such as voltage level and type of users (residential/non-residential). Then, statistical analyses and clustering techniques are applied to create appropriate groups of users and compute the load profiles for each group. This paper considers the DSO point of view and proposes the definition of additional internal categories, determined by using mono-, two- and three-dimensional features based on reference power, energy, and utilisation. The internal categories contain subsets of users with similar characteristics, which are then sent to the subsequent steps of the procedure for computing the load profiles. The internal categories are formed by executing a clustering algorithm on annual data, or adopting a consensus-based procedure that evaluates the results of clustering algorithms executed for each month of the year. The results are presented considering real data obtained during the years 2019 and 2020 for a large set of low-voltage three-phase users. The effects of the severe restrictions due to the COVID-19 pandemic on some monthly energy data in 2020 are also considered in the analysis.

关键词： Electricity clustering algorithms Low voltage Voltage COVID-19 Three-dimensional displays Statistical analysis clustering customer category electrical demand energy consumption load curve statistical analysis

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Anomaly Detection in Logs Using Deep Learning

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IEEE ACCESS 2024年 12卷 176124-176135页

作者： Aziz, Ayesha Munir, Kashif Natl Univ Comp & Emerging Sci FAST Sch Comp Islamabad 44000 Pakistan Arab Open Univ Fac Comp Studies Riyadh Saudi Arabia

Detection of abnormalities is important for the security and reliability of computer systems as they heavily rely on logs to detect anomalies. The logs provide general information, errors, warnings, and debugging information. Existing approaches for detecting anomalies are sometimes inaccurate due to their limitations related to log-processing leading to loss of semantic significance. Existing approaches, like Deeplog and LogAnomaly, have restrictions in detecting irregularities in log frameworks mainly in large dynamic systems. In this paper, we propose a hybrid anomaly detection technique that combines unsupervised approaches such as Self-Organizing Maps, Bert Encoders, and Autoencoders to handle these issues. The approach improves anomaly identification accuracy by employing semantic vectors obtained by the Bert Encoder to recognize patterns with autoencoders. The evaluation results show that the proposed strategy outperforms the existing methods for various types of data including system logs, network traffic, and financial transactions.

关键词： Anomaly detection Deep learning Semantics Encoding Bidirectional control Real-time systems Data models Pipelines Machine learning algorithms clustering algorithms classification unsupervised learning deep learning

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Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 2848-2862页

作者： Liang, Youwei Huang, Dong Wang, Chang-Dong Yu, Philip S. South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Agr Technol Trop South China Guangzhou 510642 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA

Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on 12 multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. The code is available at https://***/youweiliang/Multi-view_Graph_Learning.

关键词： Optimization clustering algorithms Linear programming Learning systems Fuses Time complexity Noise measurement Consistency data clustering efficient optimization inconsistency multi-view clustering multi-view graph learning

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Machine Learning-Based Multipath Components clustering and Cluster Characteristics Analysis in High-Speed Railway Scenarios

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第6期70卷 4027-4039页

作者： Zhou, Tao Qiao, Yuanyuan Salous, Sana Liu, Liu Tao, Cheng Beijing Jiaotong Univ Inst Broadband Wireless Mobile Commun Beijing 100044 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Univ Durham Sch Engn & Comput ing Sci Durham DH1 3LE England

Channel multipath components (MPCs) clustering and cluster characterization are the prerequisite for the development of cluster based channel models. This article investigates the MPCs clustering based on machine learning (ML) and analyzes the cluster characteristics in typical high-speed railway (HSR) scenarios. A variational Bayesian Gaussian mixture model (GMM)-based algorithm is introduced to achieve the space-time clustering of MPCs, which incorporates the statistical characteristics of MPCs and can automatically determine the optimum number of clusters. Moreover, a novel density-based validity index is proposed for evaluating the MPCs clustering performance. The proposed validity index improves the traditional index by considering the intracluster density, which can be calculated according to the Graham scanning method and Green's formula. In addition to synthetic datasets, realistic MPCs datasets collected in an HSR obstructed viaduct scenario are used for the performance evaluation of the clustering algorithm and clustering validity index. Based on clustering results in the measured scenario, static cluster characteristics, including cluster number, intercluster and intracluster delay spreads (DSs) and angle spreads (ASs), and dynamic cluster characteristics such as cluster lifetime and birth and death property of clusters, are extracted and analyzed. These results will be useful for cluster-based channel modeling in future HSR mobile communication systems.

关键词： clustering algorithms Indexes Heuristic algorithms Delays Mobile communication Mathematical models Wireless communication Cluster characteristics Gaussian mixture model (GMM) high-speed railway (HSR) scenario machine learning (ML) multipath components (MPCs) clustering

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Transfer Collaborative Fuzzy clustering in Distributed Peer-to-Peer Networks

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第2期30卷 500-514页

作者： Dang, Bozhan Wang, Yingxu Zhou, Jin Wang, Rongrong Chen, Long Chen, C. L. Philip Zhang, Tong Han, Shiyuan Wang, Lin Chen, Yuehui Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 999078 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version, with the learning-rate-adjustable strategy instead of fixed values, is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods.

关键词： clustering algorithms Collaboration Peer-to-peer computing Distributed databases clustering methods Prototypes Partitioning algorithms Adjustable learning rate attribute-weight-entropy regularization collaborative fuzzy clustering distributed peer-to-peer network transfer learning

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