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CoIn: Correlation Induced clustering for Cognition of High Dimensional Bioinformatics Data

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第2期27卷 598-607页

作者： Zeng, Zeng Zhao, Ziyuan Xu, Kaixin Li, Yangfan Chen, Cen Zou, Xiaofeng Wang, Yulan Wei, Wei Chow, Pierce K. H. Li, Xiaoli Shanghai Univ Sch Microelect Shanghai Peoples R China Inst Infocomm Res Agcy Sci Technol & Res ASTAR Singapore Singapore Hunan Univ Coll Informat Sci & Engn Changsha Hunan Peoples R China Nanyang Technol Univ Singapore Singapore Univ Technol Sch Comp Sci & Engn Xian Peoples R China Duke NUS Med Sch Singapore Singapore

Analysis of high dimensional biomedical data such as microarray gene expression data and mass spectrometry images, is crucial to provide better medical services including cancer subtyping, protein homology detection, etc. clustering is a fundamental cognitive task which aims to group unlabeled data into multiple clusters based on their intrinsic similarities. However, for most clustering methods, including the most widely used K-means algorithm, all features of the high dimensional data are considered equally in relevance, which distorts the performance when clustering high-dimensional data where there exist many redundant variables and correlated variables. In this paper, we aim at addressing the problem of the high dimensional bioinformatics data clustering and propose a new correlation induced clustering, CoIn, to capture complex correlations among high dimensional data and guarantee the correlation consistency within each cluster. We evaluate the proposed method on a high dimensional mass spectrometry dataset of liver cancer tumor to explore the metabolic differences on tissues and discover the intra-tumor heterogeneity (ITH). By comparing the results of baselines and ours, it has been found that our method produces more explainable and understandable results for clinical analysis, which demonstrates the proposed clustering paradigm has the potential with application to knowledge discovery in high dimensional bioinformatics data.

关键词： Correlation Bioinformatics Tumors clustering algorithms clustering methods Biomedical imaging Kernel clustering correlation analysis correlation induced clustering

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Efficient Storage Management for Social Network Events Based on clustering and Hot/Cold Data Classification

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第1期10卷 120-130页

作者： Xie, Yulai Tong, Shuai Zhou, Pan Li, Yuli Feng, Dan Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Hubei Engn Res Ctr Big Data Secur Wuhan 430074 Peoples R China Minist Educ China Wuhan Natl Lab Optoelect Key Lab Informat Storage Syst Engn Res Ctr Data Storage Syst & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

Social network events are related to the national economy and people's livelihood, so timely perception and processing of massive social network events data are becoming increasingly important for public opinion analysis. How to fully exploit the access feature of event information to store and manage social network events is of great significance for accurate and real-time query analysis. We propose a social network event storage management method based on microblog text clustering and hot/cold data classification. First, for the microblog text data, we construct a keyword provenance graph by using the information entropy to measure the weight of the edge between keyword nodes. Then, we cluster the events using the provenance-based community partition (PCP) with local modularity to improve the event clustering accuracy. In addition, we can further filter noisy data via incremental clustering, enable hot/cold event data classification and dynamic migration, and compress cold data to save space on a hybrid storage architecture. The experimental results show that the clustering purity can reach more than 93% and the query time can be reduced by more than 70% using clustering and hybrid storage policy.

关键词： Blogs Social networking (online) clustering algorithms Mutual information Partitioning algorithms Storage management Semisupervised learning Data provenance event clustering hot cold data classification social network event

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Density-Based Affinity Propagation Tensor clustering for Intelligent Fault Diagnosis of Train Bogie Bearing

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第6期24卷 6053-6064页

作者： Wei, Zexian He, Deqiang Jin, Zhenzhen Liu, Bin Shan, Sheng Chen, Yanjun Miao, Jian Guangxi Univ Sch Mech Engn Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Zhuzhou CRRC Times Elect Co Ltd Zhuzhou 412001 Peoples R China

Health monitor of bogie-bearing on the train can ensure constant operation of the rail transit system. Since the metro or other rail transit have high safety requirements, it is hard to acquire numerous fault samples. Besides, diagnosing train bogie-bearings under variable working conditions is challenging due to wheel-rail coupling, speed variation, and load fluctuation. An intelligent approach for bogie-bearing fault diagnosis is proposed to deal with the above problems. A third-order tensor model is established to be suitable for variable working conditions. Furthermore, a density-based affinity propagation tensor (DAP-Tensor) clustering algorithm is presented to identify different failures with unlabeled. Train bogie and public data sets were employed to simulate three probable conditions of train operation: high-frequency impact, speed variation, and load change. Compared with existing clustering methods in three cases, the proposed DAP-Tensor performs better in identifying bearing faults under variable working conditions. Moreover, The DAP-tensor has a comparable recognition rate to some deep learning methods, which unsupervised characteristics show it has potential for applications on rail transit trains.

关键词： Tensors Fault diagnosis clustering algorithms Signal processing algorithms Monitoring Rails Time complexity Bogie-bearing tensor clustering variable working conditions intelligent fault diagnosis

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Density-Based clustering by Means of Bridge Point Identification

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第11期35卷 11274-11287页

作者： Colomba, Luca Cagliero, Luca Garza, Paolo Politecn Torino Dipartimento Automat & Informat I-10129 Turin Italy

Density-based clustering focuses on defining clusters consisting of contiguous regions characterized by similar densities of points. Traditional approaches identify core points first, whereas more recent ones initially identify the cluster borders and then propagate cluster labels within the delimited regions. Both strategies encounter issues in presence of multi-density regions or when clusters are characterized by noisy borders. To overcome the above issues, we present a new clustering algorithm that relies on the concept of bridge point. A bridge point is a point whose neighborhood includes points of different clusters. The key idea is to use bridge points, rather than border points, to partition points into clusters. We have proved that a correct bridge point identification yields a cluster separation consistent with the expectation. To correctly identify bridge points in absence of a priori cluster information we leverage an established unsupervised outlier detection algorithm. Specifically, we empirically show that, in most cases, the detected outliers are actually a superset of the bridge point set. Therefore, to define clusters we spread cluster labels like a wildfire until an outlier, acting as a candidate bridge point, is reached. The proposed algorithm performs statistically better than state-of-the-art methods on a large set of benchmark datasets and is particularly robust to the presence of intra-cluster multiple densities and noisy borders.

关键词： Bridges clustering algorithms Partitioning algorithms Noise measurement Anomaly detection Benchmark testing Optics Density-based clustering outlier detection unsupervised learning

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Point-Set Kernel clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第5期35卷 5147-5158页

作者： Ting, Kai Ming Wells, Jonathan R. Zhu, Ye Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia

Measuring similarity between two objects is the core operation in existing clustering algorithms in grouping similar objects into clusters. This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects. The proposed clustering procedure utilizes this new measure to characterize every cluster grown from a seed object. We show that the new clustering procedure is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering algorithms are either efficient or effective. In comparison with the state-of-the-art density-peak clustering and scalable kernel k-means clustering, we show that the proposed algorithm is more effective and runs orders of magnitude faster when applying to datasets of millions of data points, on a commonly used computing machine.

关键词： Kernel clustering algorithms Density measurement Size measurement Computational efficiency Approximation algorithms Shape Cluster analysis kernel clustering point-set kernel data dependent kernel

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Consistent Multiple Graph Embedding for Multi-View clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 1008-1018页

作者： Wang, Yiming Chang, Dongxia Fu, Zhiqiang Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Technol Beijing 100044 Peoples R China

Graph-based multi-view clustering aiming to obtain a partition of data across multiple views, has received considerable attention in recent years. Although great efforts have been made for graph-based multi-view clustering, it is still challenging to fuse characteristics from various views to learn a common representation for clustering. In this paper, we propose a novel Consistent Multiple Graph Embedding clustering framework (CMGEC). Specifically, a multiple graph auto-encoder (M-GAE) is designed to flexibly encode the complementary information of multi-view data using a multi-graph attention fusion encoder. To guide the learned common representation maintaining the similarity of the neighboring characteristics in each view, a Multi-view Mutual Information Maximization module (MMIM) is introduced. Furthermore, a graph fusion network (GFN) is devised to explore the relationship among graphs from different views and provide a common consensus graph needed in M-GAE. By jointly training these models, the common representation can be obtained, which encodes more complementary information from multiple views and depicts data more comprehensively. Experiments on three types of multi-view datasets demonstrate CMGEC outperforms the state-of-the-art clustering methods.

关键词： Mutual information clustering methods clustering algorithms Data models Task analysis Laplace equations Fuses Graph neural networks multi-view clustering mutual information representation learning

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K-Means and Alternative clustering Methods in Modern Power Systems

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IEEE ACCESS 2023年 11卷 119596-119633页

作者： Miraftabzadeh, Seyed Mahdi Colombo, Cristian Giovanni Longo, Michela Foiadelli, Federica Politecn Milan Dept Energy I-20156 Milan Italy

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.

关键词： clustering algorithms K-means clustering power systems

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STAR-FC: Structure-Aware Face clustering on Ultra-Large-Scale Graphs

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第11期45卷 14005-14019页

作者： Shen, Shuai Li, Wanhua Zhu, Zheng Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Face clustering is a promising method for annotating unlabeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer from the limitation of training data scale, while local-based ones are inefficient for inference due to the use of numerous overlapped subgraphs. Previous approaches fail to tackle these two challenges simultaneously. To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face clustering (STAR-FC) method. Specifically, we design a structure-preserving subgraph sampling strategy to explore the power of large-scale training data, which can increase the training data scale from 10(5) to 10(7). On this basis, a novel hierarchical GCN training paradigm is further proposed for better capturing the dynamic local structure. During inference, the STAR-FC performs efficient full-graph clustering with two steps: graph parsing and graph refinement. And the concept of node intimacy is introduced in the second step to mine the local structural information, where a calibration module is further proposed for fairer edge scores. The STAR-FC gets 93.21 pairwise F-score on standard partial MS1M within 312 seconds, which far surpasses the state-of-the-arts while maintaining high inference efficiency. Furthermore, we are the first to train on an ultra-large-scale graph with 20 M nodes, and achieve superior inference results on 12 M testing data. Overall, as a simple and effective method, the proposed STAR-FC provides a strong baseline for large-scale face clustering.

关键词： Training Faces Face recognition Training data Standards clustering algorithms Task analysis Face clustering large-scale graph graph convolutional network hierarchical GCN training node intimacy

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A Density Peaking clustering Algorithm for Differential Privacy Preservation

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IEEE ACCESS 2023年 11卷 54240-54253页

作者： Chen, Hua Mei, Kehui Zhou, Yuan Wang, Nan Tang, Mengdi Cai, Guangxing Hubei Univ Technol Sch Sci Wuhan 430068 Peoples R China

The privacy protection problem in data mining has received increasingly attention and is a hot topic of current research. To address the problems of large accuracy loss and instability of clustering results of clustering algorithms under differential privacy protection requirements, a density peak clustering algorithm for differential privacy protection (DP-chDPC) is proposed. Firstly, the original DPC algorithm is improved, by using the dichotomy method to automatically determine the truncation distance to avoid the subjectivity of manual selection, and by setting the threshold of local density and center offset distance to automatically obtain the clustering center, which overcomes the uncertainty of the original DPC algorithm to select the clustering center based on the decision graph. Then, noise is added to the local density by using the Laplace mechanism to realize the differential privacy protection of the algorithm during the clustering analysis. Finally, the Chebyshev distance is used to replace the Euclidean distance to calculate the distance matrix, which reduces the interference on the clustering results after the algorithm adds noise, and reduces the loss of clustering accuracy, so that the stability of the algorithm is improved. The experimental results show that the DP-chDPC algorithm can effectively reduce the loss of clustering accuracy after the algorithm adds noise, and the clustering results are more stable.

关键词： clustering algorithms Differential privacy Privacy Stability analysis Sensitivity Resource management Partitioning algorithms Cluster analysis differential privacy Chebyshev distance dichotomous method Laplace mechanism

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Deep clustering and Visualization for End-to-End High-Dimensional Data Analysis

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第11期34卷 8543-8554页

作者： Wu, Lirong Yuan, Lifan Zhao, Guojiang Lin, Haitao Li, Stan Z. Westlake Univ Sch Engn AI Lab Hangzhou 310000 Peoples R China

High-dimensional data analysis for exploration and discovery includes two fundamental tasks: deep clustering and data visualization. When these two associated tasks are done separately, as is often the case thus far, disagreements can occur among the tasks in terms of geometry preservation. Namely, the clustering process is often accompanied by the corruption of the geometric structure, whereas visualization aims to preserve the data geometry for better interpretation. Therefore, how to achieve deep clustering and data visualization in an end-to-end unified framework is an important but challenging problem. In this article, we propose a novel neural network-based method, called deep clustering and visualization (DCV), to accomplish the two associated tasks end-to-end to resolve their disagreements. The DCV framework consists of two nonlinear dimensionality reduction (NLDR) transformations: 1) one from the input data space to latent feature space for clustering and 2) the other from the latent feature space to the final 2-D space for visualization. Importantly, the first NLDR transformation is mainly optimized by one clustering Loss, allowing arbitrary corruption of the geometric structure for better clustering, while the second NLDR transformation is optimized by one Geometry-Preserving Loss to recover the corrupted geometry for better visualization. Extensive comparative results show that the DCV framework outperforms other leading clustering-visualization algorithms in terms of both quantitative evaluation metrics and qualitative visualization.

关键词： Data visualization Geometry clustering algorithms Task analysis Data analysis Neural networks Manifolds Data visualization deep clustering geometry preservation neural networks unsupervised learning

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