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Dynamic Sparse Subspace clustering for Evolving High-Dimensional Data Streams

IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 4173-4186页

作者： Sui, Jinping Liu, Zhen Liu, Li Jung, Alexander Li, Xiang Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Aalto Univ Dept Comp Sci Espoo 02150 Finland Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 02150 Finland Natl Univ Def Technol Dept Elect Sci Changsha 410073 Peoples R China

In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.

关键词： clustering algorithms Indexes Heuristic algorithms Data models Adaptation models Task analysis Data structures Data stream clustering (DSC) high-dimensional data stream sparse representation subspace clustering (SC)

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General Fuzzy C-Means clustering Strategy: Using Objective Function to Control Fuzziness of clustering Results

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第9期30卷 3601-3616页

作者： Zhao, Kaixin Dai, Yaping Jia, Zhiyang Ji, Ye Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China

As one of the most commonly used clustering methods, the fuzzy C-means (FCM) clustering strategy extends the notion of hard clustering to associate each pattern with every cluster using a membership function. Although a lot of efforts have been made by the clustering community, it is still unclear how to evaluate the fuzziness of different versions of FCM. To fill this theoretical blank, by observing a family of objective functions, a definition of fuzzy degree is provided to quantify the fuzziness of different versions of FCM according to their dedicated objective functions. Then, a general fuzzy C-means (GFCM) clustering algorithm is proposed to solve the clustering problem of using FCM under different distance metrics and fuzzy degrees. From the perspective of fuzzy attribute, the using of this fuzzy degree can be used to reveal the essential difference among a group of FCM-based clustering algorithms, and the proposed GFCM achieves the aim of using objective functions to control the fuzziness of clustering results. Additionally, some properties and relations of these FCM-based clustering algorithms under different fuzzy degrees are discussed and the convergence and stability of the proposed GFCM are proved. Finally, extensive experiments have been performed to demonstrate that by comparison with the effect caused by distance metric, the choices of fuzzy degree have a more significant effect and improvement on the performances of a FCM-based clustering algorithm.

关键词： Linear programming clustering algorithms Prototypes Distributed databases Extraterrestrial measurements Optimization Euclidean distance Family of objective functions fuzzy C-means (FCM) fuzzy clustering fuzzy degree

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Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation

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IEEE ACCESS 2024年 12卷 11761-11773页

作者： Rykov, Andrei de Amorim, Renato Cordeiro Makarenkov, Vladimir Mirkin, Boris Natl Res Univ Higher Sch Econ Dept Data Anal & Machine Intelligence Moscow 101000 Russia Univ Essex Comp Sci & Elect Engn Dept Wivenhoe CO4 3SQ England Imagia Cybernet Montreal PQ H3C 3P8 Canada Mila Quebec AI Inst Montreal PQ H2S 3H1 Canada Univ London Dept Comp Sci & Informat Syst London WC1E 7HX England

This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion for determining the number of clusters, K, in datasets, using the popular Silhouette width index as a benchmark. Our experiments involve a novel version of the Elbow index, defined using values of K two or three steps apart. We also discuss alternative ways of computing the inertia and summarizing its values. Even though there are no overall winners in our experiments, some of our results are very conclusive and can be used as a guide for indices determining the number of clusters in K-means.

关键词： Indexes clustering algorithms Euclidean distance Amplitude modulation Partitioning algorithms Computer science K-means number of clusters inertia elbow method Calinski-Harabasz index Hartigan rule

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clustering Product Development Project Organization From the Perspective of Social Network Analysis

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2022年第6期69卷 2482-2496页

作者： Yang, Qing Yang, Na Browning, Tyson R. Jiang, Bin Yao, Tao Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China Texas Christian Univ Neeley Sch Business Ft Worth TX 76129 USA Depaul Univ Driehaus Coll Business Chicago IL 60604 USA Penn State Univ Harold & Inge Marcus Dept Ind & Mfg Engn University Pk PA 16802 USA

In product development (PD) organizations, coordinating technical dependencies among teams with different expertise in overlapping processes is a fundamental challenge. This article takes a more sophisticated approach than prior methodologies to improve coordination via organizational clustering, by accounting for both team structural and attribute similarity from the perspective of social network analysis. We built models to quantify the impact of the overlapping processes on the interaction strength among PD teams, which we then used to construct structural similarity by combining tie strength and social cohesion among teams via the design structure matrix. To evaluate the organization network, we propose social embeddedness-related centrality indices within (intracluster) and across (intercluster) team groupings. To facilitate knowledge sharing, we base team attribute similarity on product- and process-related expertise among teams. We integrate the modularity index and an improved silhouette index to find an optimal number of clusters, which we then incorporate with team similarity measures as inputs to a spectral clustering algorithm. An industrial example illustrates the proposed model. The clustering results reinforce several managerial practices but also yield new insights, such as how to measure similarity among teams based on organizational network characteristics and how structural and attribute similarities impact the optimal organizational structure.

关键词： Organizations clustering algorithms Social networking (online) Product development Indexes Complexity theory Analytical models clustering algorithm design structure matrix (DSM) organization design product development (PD) project management social network analysis (SNA)

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Adaptive Mixed-Attribute Data clustering Method Based on Density Peaks

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COMPLEXITY 2022年第0期2022卷

作者： Liu, Shihua Wenzhou Polytech Sch Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Network Secur Detect & Protect Engn Techn Wenzhou 325035 Peoples R China

The clustering of mixed-attribute data is a vital and challenging issue. The density peaks clustering algorithm brings us a simple and efficient solution, but it mainly focuses on numerical attribute data clustering and cannot be adaptive. In this paper, we studied the adaptive improvement method of such an algorithm and proposed an adaptive mixed-attribute data clustering method based on density peaks called AMDPC. In this algorithm, we used the unified distance metric of mixed-attribute data to construct the distance matrix, calculated the local density based on K-nearest neighbors, and proposed the automatic determination method of cluster centers based on three inflection points. Experimental results on real University of California-Irvine (UCI) datasets showed that the proposed AMDPC algorithm could realize adaptive clustering of mixed-attribute data, can automatically obtain the correct number of clusters, and improved the clustering accuracy of all datasets by more than 22.58%, by 24.25%, by 28.03%, by 22.5%, and by 10.12% for the Heart, Cleveland, Credit, Acute, and Adult datasets compared to that of the traditional K-prototype algorithm, respectively. It also outperformed a modified density peaks clustering algorithm for mixed-attribute data (DPC_M) algorithms.

关键词： clustering algorithms

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Rapid determination of lambda-cyhalothrin residues on Chinese cabbage based on MIR spectroscopy and a Gustafson-Kessel noise clustering algorithm

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RSC ADVANCES 2022年第29期12卷 18457-18465页

作者： Zheng, Jun Gong, Zhe Yin, Shaojie Wang, Wei Wang, Meng Lin, Peng Zhou, Haoxiang Yang, Yangjian Zhejiang Univ Taizhou Dept Elect & Control Engn Res Inst Taizhou 318000 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212000 Jiangsu Peoples R China

Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health. In order to quickly, non-destructively and effectively qualitatively analyze lambda-cyhalothrin residues on Chinese cabbage, a method involving a Gustafson-Kessel noise clustering (GKNC) algorithm was proposed to cluster the mid-infrared (MIR) spectra. A total of 120 Chinese cabbage samples with three different lambda-cyhalothrin residue levels (no lambda-cyhalothrin, and cases where the ratios of lambda-cyhalothrin and water were 1 : 500 and 1 : 100) were scanned using an Agilent Cary 630 FTIR spectrometer for collecting the MIR spectra. Next, multiple scatter correction (MSC) was employed to eliminate the effects of light scattering. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) were utilized to reduce the dimensionality and extract the feature information from the MIR spectra. Finally, fuzzy c-means (FCM) clustering, Gustafson-Kessel (GK) clustering, noise clustering (NC) and the GKNC algorithm were applied to cluster the MIR spectral data, respectively. The experimental results showed that the GKNC algorithm gave the best classification performance compared against the other three fuzzy clustering algorithms, and its highest clustering accuracy reached 93.3%. Therefore, the GKNC algorithm coupled with MIR spectroscopy is an effective method for detecting lambda-cyhalothrin residues on Chinese cabbage.

关键词： clustering algorithms

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Weak Augmentation Guided Relational Self-Supervised Learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 8502-8516页

作者： Zheng, Mingkai You, Shan Wang, Fei Qian, Chen Zhang, Changshui Wang, Xiaogang Xu, Chang Univ Sydney Sch Comp Sci Fac Engn Camperdown NSW 2050 Australia SenseTime Res Shanghai 200233 Peoples R China Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Automat Inst Artificial Intelligence Beijing 100190 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most methods mainly focus on the instance level information (i.e., the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduce a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as relation metric, which is thus utilized to match the feature embeddings of different augmentations. To boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. The designed asymmetric predictor head and an InfoNCE warm-up strategy enhance the robustness to hyper-parameters and benefit the resulting performance. Experimental results show that our proposed ReSSL substantially outperforms the state-of-the-art methods across different network architectures, including various lightweight networks (e.g., EfficientNet and MobileNet).

关键词： Task analysis Training clustering algorithms Visualization Supervised learning Representation learning Memory management Contrastive learning unsupervised learning self-supervised learning representation learning

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Spatial Contiguity-Constrained Hierarchical clustering for Traffic Prediction in Bike Sharing Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第6期23卷 5754-5764页

作者： Kim, Kyoungok Seoul Natl Univ Sci & Technol SeoulTech Int Fus Sch Informat Technol Management Programme Seoul 01811 South Korea

The critical problem in managing a bike sharing system (BSS) is to solve the imbalance in the number of available bikes by stations and times, which negatively affects the users' riding experience. To address this issue, many BSS operators rearrange bikes using a fleet of trucks. Moreover, the effectiveness and efficiency of rebalancing operations heavily rely on accurate traffic prediction in BSS. Hence, many researchers have developed methods to enhance the prediction performance. One approach to increase the accuracy is to hierarchically predict demand from higher level (cluster or system) to station level. In the hierarchical prediction frameworks, the cluster-level to predict the demand of the total usage of groups of stations is introduced and it is crucial to partition stations into clusters that can increase the stability of actual usage and prediction. Therefore, the present work proposes a new spatial contiguity-constrained hierarchical clustering algorithm for BSS. The proposed algorithm is based on hierarchical clustering, which is deterministic, and uses the hourly proportions of check-outs and check-ins to define the temporal usage pattern of each station that does not require cluster assignment. Thus, different from existing methods, our proposed algorithm is deterministic and fast. In addition, the proposed algorithm is proven to be superior to other clustering methods in terms of traffic prediction at both the cluster and station levels.

关键词： clustering algorithms Prediction algorithms Predictive models Heuristic algorithms Convergence clustering methods System analysis and design Bike sharing system traffic prediction hierarchical prediction framework hierarchical clustering random forest

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Statistical Analysis of Microarray Data clustering using NMF, Spectral clustering, Kmeans, and GMM

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第2期19卷 1173-1192页

作者： Mirzal, Andri King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia

In unsupervised learning literature, the study of clustering using microarray gene expression datasets has been extensively conducted with nonnegative matrix factorization (NMF), spectral clustering, kmeans, and gaussian mixture model (GMM)are some of the most used methods. However, there is still a limited number of works that utilize statistical analysis to measure the significances of performance differences between these methods. In this paper, statistical analysis of performance differences between ten NMF, six spectral clustering, four GMM, and the standard kmeans algorithms in clustering eleven publicly available microarray gene expression datasets with the number of clusters ranges from two to ten is presented. The experimental results show that statistically NMFs and kmeans have similar performances and outperform spectral clustering. As spectral clustering can be used to uncover hidden manifold structures, the underperformance of spectral methods leads us to question whether the datasets have manifold structures. Visual inspection using multidimensional scaling plots indicates that such structures do not exist. Moreover, as the plots indicate that clusters in some datasets have elliptical boundaries, GMM methods are also utilized. The experimental results show that GMM methods outperform the other methods to some degree, and thus imply that the datasets follow gaussian distributions.

关键词： clustering algorithms Data models Matrix decomposition Task analysis Statistical analysis Standards clustering methods clustering gmm kmeans microarray data NMF spectral method statistical analysis

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clustering Methods for Particle Filters With Gaussian Mixture Models

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第2期58卷 1109-1118页

作者： Yun, Sehyun Zanetti, Renato Univ Texas Austin Austin TX 78712 USA

New clustering methods are proposed to develop novel particle filters with Gaussian mixture models (PFGMM). In the PFGMM, the propagated samples are clustered to recover a Gaussian mixture model (GMM) using a clustering algorithm, which plays a fundamental role in the filter's performance. Two clustering methods are introduced that simultaneously minimize the covariance of each of the GMM components and maximize the likelihood function. Under the scenarios considered in this article, it is shown through numerical simulation that the PFGMMs with the proposed clustering algorithms lead to better performance than the PFGMM employing the K-means or the expectation-maximization algorithms as well as the regularized particle filter (PF) and the Gaussian sum PF.

关键词： clustering algorithms Heuristic algorithms Standards clustering methods Linear programming Covariance matrices Time measurement

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