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Two-Stage Sparse Representation clustering for Dynamic Data Streams

IEEE TRANSACTIONS ON CYBERNETICS 2023年第10期53卷 6408-6420页

作者： Chen, Jie Wang, Zhu Yang, Shengxiang Mao, Hua Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Law Sch Chengdu 610065 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Data streams are a potentially unbounded sequence of data objects, and the clustering of such data is an effective way of identifying their underlying patterns. Existing data stream clustering algorithms face two critical issues: 1) evaluating the relationship among data objects with individual landmark windows of fixed size and 2) passing useful knowledge from previous landmark windows to the current landmark window. Based on sparse representation techniques, this article proposes a two-stage sparse representation clustering (TSSRC) method. The novelty of the proposed TSSRC algorithm comes from evaluating the effective relationship among data objects in the landmark windows with an accurate number of clusters. First, the proposed algorithm evaluates the relationship among data objects using sparse representation techniques. The dictionary and sparse representations are iteratively updated by solving a convex optimization problem. Second, the proposed TSSRC algorithm presents a dictionary initialization strategy that seeks representative data objects by making full use of the sparse representation results. This efficiently passes previously learned knowledge to the current landmark window over time. Moreover, the convergence and sparse stability of TSSRC can be theoretically guaranteed in continuous landmark windows under certain conditions. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of TSSRC.

关键词： clustering algorithms Dictionaries Heuristic algorithms Machine learning Streaming media Data models Convergence clustering data stream dictionary learning sparse representation

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Autoencoder-Based Iterative Modeling and Multivariate Time-Series Subsequence clustering Algorithm

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IEEE ACCESS 2023年 11卷 18868-18886页

作者： Kohne, Jonas Henning, Lars Guhmann, Clemens Tech Univ Berlin Chair Elect Measurement & Diagnost Technol D-10623 Berlin Germany IAV GmbH D-10587 Berlin Germany

This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become increasingly important due to growing availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training Condition-based Maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data, which could be used for training a CbM model, would emerge. Therefore, we introduce an algorithm that uses a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems.

关键词： clustering algorithms Mechatronics Labeling Data models Transient analysis Maintenance engineering Terminology Condition-based maintenance multivariate time-series data change point detection unsupervised clustering autoencoder segmentation subsequence clustering

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Spectral clustering With Adaptive Neighbors for Deep Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第4期34卷 2068-2078页

作者： Zhao, Yang Li, Xuelong Northwestern Polytech Univ 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

Spectral clustering is a well-known clustering algorithm for unsupervised learning, and its improved algorithms have been successfully adapted for many real-world applications. However, traditional spectral clustering algorithms are still facing many challenges to the task of unsupervised learning for large-scale datasets because of the complexity and cost of affinity matrix construction and the eigen-decomposition of the Laplacian matrix. From this perspective, we are looking forward to finding a more efficient and effective way by adaptive neighbor assignments for affinity matrix construction to address the above limitation of spectral clustering. It tries to learn an affinity matrix from the view of global data distribution. Meanwhile, we propose a deep learning framework with fully connected layers to learn a mapping function for the purpose of replacing the traditional eigen-decomposition of the Laplacian matrix. Extensive experimental results have illustrated the competitiveness of the proposed algorithm. It is significantly superior to the existing clustering algorithms in the experiments of both toy datasets and real-world datasets.

关键词： Laplace equations clustering algorithms Deep learning Unsupervised learning Complexity theory Task analysis Scalability Anchor graph neural networks spectral clustering unsupervised learning

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Multi-View clustering for Integration of Gene Expression and Methylation Data With Tensor Decomposition and Self-Representation Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 2050-2063页

作者： Gao, Xiaowei Wang, Yan Hou, Weimin Liu, Zaiyi Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xidian Univ Dept Lib Xian 710071 Shaanxi Peoples R China Northwest Minzu Univ Minist Educ Key Lab Chinas Ethn Languages Informat Technol Lanzhou 730030 Gansu Peoples R China Hebei Univ Sci & Technol Sch Informat Sci & Engn Dept Radiol Shijiazhuang 050018 Hebei Peoples R China Guangdong Gen Hosp Guangdong Acad Med Sci Dept Radiol Guangzhou 510080 Peoples R China

The accumulated DNA methylation and gene expression provide a great opportunity to exploit the epigenetic patterns of genes, which is the foundation for revealing the underlying mechanisms of biological systems. Current integrative algorithms are criticized for undesirable performance because they fail to address the heterogeneity of expression and methylation data, and the intrinsic relations among them. To solve this issue, a novel multi-view clustering with self-representation learning and low-rank tensor constraint (MCSL-LTC) is proposed for the integration of gene expression and DNA methylation data, which are treated as complementary views. Specifically, MCSL-LTC first learns the low-dimensional features for each view with the linear projection, and then these features are fused in a unified tensor space with low-rank constraints. In this case, the complementary information of various views is precisely captured, where the heterogeneity of omic data is avoided, thereby enhancing the consistency of different views. Finally, MCSL-LTC obtains a consensus cluster of genes reflecting the structure and features of various views. Experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines in terms of accuracy on both the social and cancer data, which provides an effective and efficient method for the integration of heterogeneous genomic data.

关键词： DNA Epigenetics Bioinformatics Genomics Gene expression clustering algorithms Tensors DNA methylation gene clustering low-rank constraint multi-view clustering self-representation learning

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scHOIS: Determining Cell Heterogeneity Through Hierarchical clustering Based on Optimal Imputation Strategy

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第2期20卷 1431-1444页

作者： Cheng, Xiaoqing Yan, Chang Jiang, Hao Qiu, Yushan Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen 518000 Guangdong Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China

Advances in single-cell RNA sequencing (scRNA-seq) technology provide an unbiased and high-throughput analysis of each cell at single-cell resolution, and further facilitate the development of cellular heterogeneity analysis. Despite the promise of scRNA-seq, the data generated by this method are sparse and noisy because of the presence of dropout events, which can greatly impact downstream analyses such as differential gene expression, cell type annotation, and linage trajectory reconstruction. The development of effective and robust computational methods to address both dropout and clustering are thus urgently needed. In this study, we propose a flexible, accurate two-stage algorithm for single cell heterogeneity analysis via hierarchical clustering based on an optimal imputation strategy, called scHOIS. At the first stage, masked non-negative matrix factorization is applied to approximate the original observed scRNA-seq data, with optimal rank determined by variance analysis. At the second stage, hierarchical clustering is applied to group the imputed cells using Pearson correlation to measure similarity, with the optimal number of clusters determined by integrating three classical indexes. We performed extensive experiments on real-world datasets, which showed that scHOIS effectively and robustly distinguished cellular differences and that the clustering performance of this algorithm was superior to that of other state-of-the-art methods.

关键词： clustering algorithms Sparse matrices Gene expression Data models Computational modeling RNA Mathematical models Single cell RNA-sequencing matrix completion imputation clustering

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Distributed Auto-clustering for Residential Load Profiling Using AMI Data From the US High Plains

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IEEE TRANSACTIONS ON SMART GRID 2023年第6期14卷 4530-4541页

作者： Vahedi, Soroush Zhao, Long South Dakota Sch Mines & Technol Elect Engn & Comp Sci Dept Rapid City SD 57701 USA

Managing demand and building customized services are essential for power retailers, distribution network operators (DNOs), and load-serving entities (LSEs) in a competitive retail environment. Therefore, understanding residential customer power demand behaviors is essential for accurate load profiling. However, the large volume of fine-grained smart meter data and the wide variety of residential behavior make load profile extraction extremely challenging, even further exacerbated by deterministic clustering's inherent limitations. This paper develops a distributed auto-clustering approach (DAA) for load profiling using real smart meter data based on a divide-and-conquer scheme to tackle current load profiling challenges. To improve the load profiling accuracy, temperature-based categories (TBCs) are proposed to distinguish days with similar temperatures. A Heterogeneous Distributed Local Sites (HDLS) scheme is developed based on the raster map concept to distribute smart meter data among local sites while considering their size and density. Local sites send the load representatives to a central site for global clustering to significantly reduce clustering complexity and computational burden. To verify the proposed DAA, approximately 11,000 residential 15-minute smart meter data provided by a utility company from the U.S. high plains were utilized in this paper.

关键词： Smart meters clustering algorithms Power demand Hardware design languages Distributed databases Behavioral sciences Temperature distribution Auto-clustering AMI clustering distributed clustering large-dataset load profiling residential load smart meter

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Cloud services cost comparison: a clustering analysis framework

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COMPUTING 2023年第10期105卷 2061-2088页

作者： Fragiadakis, George Liagkou, Vasiliki Filiopoulou, Evangelia Fragkakis, Dimitris Michalakelis, Christos Nikolaidou, Mara Harokopio Univ Athens Dept Informat & Telemat Omirou 9 Tavros 17778 Greece

As cloud computing has grown in popularity, several different models and deployment strategies have emerged, fulfilling specific needs of different users. Thus cloud service selection is a major task that combines business and technical aspects. Several cloud providers categorize their bundles to help users find the best fit. However, the policy behind this categorization is unclear, making it difficult to be consistent among providers. Therefore, this work explores the complication of selecting the optimal cloud service among numerous and comparable solutions and introduces a selection framework for cloud services based on clustering analysis, providing an overall size categorization of cloud services derived from the cloud market and making the categorization policy explicit and homogeneous across all providers. Finally, a real-word case study is presented, highlighting the application of the proposed framework.

关键词： Techno-economics Costing Cloud selection framework k-means clustering algorithms

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Fuzzy-Rough Intrigued Harmonic Discrepancy clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第10期31卷 3305-3318页

作者： Yue, Guanli Qu, Yanpeng Yang, Longzhi Shang, Changjing Deng, Ansheng Chao, Fei Shen, Qiang Dalian Maritime Univ Informat Technol Coll Dalian 116026 Peoples R China Dalian Maritime Univ Coll Artificial Intelligence Dalian 116026 Peoples R China Northumbria Univ Dept Comp & Informat Sci London E1 7HT England Aberystwyth Univ Fac Business & Phys Sci Dept Comp Sci Aberystwyth SY23 3DB Wales Xiamen Univ Dept Comp Sci Xiamen 361005 Peoples R China

Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates the comparable performance for knowledge exploitation under the circumstance of information incompleteness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process;a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters, which are challenging to be determined for different data types. This article proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modeling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.

关键词： clustering algorithms Rough sets Harmonic analysis Noise measurement Fuzzy sets Uncertainty Phase change materials clustering fuzzy-rough set harmonic discrepancy rough set

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Nonsmooth Optimization-Based Model and Algorithm for Semisupervised clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5517-5530页

作者： Bagirov, Adil M. Taheri, Sona Bai, Fusheng Zheng, Fangying Federat Univ Australia Sch Engn Informat Technol & Phys Sci Ballarat Vic 3353 Australia RMIT Univ Sch Sci Melbourne Vic 3000 Australia Chongqing Normal Univ Natl Ctr Appl Math Chongqing Chongqing 401331 Peoples R China Zhejiang Sci Tech Univ Dept Math Sci Hangzhou 310018 Peoples R China

Using a nonconvex nonsmooth optimization approach, we introduce a model for semisupervised clustering (SSC) with pairwise constraints. In this model, the objective function is represented as a sum of three terms: the first term reflects the clustering error for unlabeled data points, the second term expresses the error for data points with must-link (ML) constraints, and the third term represents the error for data points with cannot-link (CL) constraints. This function is nonconvex and nonsmooth. To find its optimal solutions, we introduce an adaptive SSC (A-SSC) algorithm. This algorithm is based on the combination of the nonsmooth optimization method and an incremental approach, which involves the auxiliary SSC problem. The algorithm constructs clusters incrementally starting from one cluster and gradually adding one cluster center at each iteration. The solutions to the auxiliary SSC problem are utilized as starting points for solving the nonconvex SSC problem. The discrete gradient method (DGM) of nonsmooth optimization is applied to solve the underlying nonsmooth optimization problems. This method does not require subgradient evaluations and uses only function values. The performance of the A-SSC algorithm is evaluated and compared with four benchmarking SSC algorithms on one synthetic and 12 real-world datasets. Results demonstrate that the proposed algorithm outperforms the other four algorithms in identifying compact and well-separated clusters while satisfying most constraints.

关键词： clustering algorithms Partitioning algorithms Optimization Measurement Costs Semisupervised learning Linear programming Cluster analysis discrete gradient method (DGM) nonsmooth optimization pairwise constraints semisupervised clustering (SSC)

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Deep Latent Space clustering for Detection of Stealthy False Data Injection Attacks Against AC State Estimation in Power Systems

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IEEE TRANSACTIONS ON SMART GRID 2023年第3期14卷 2338-2351页

作者： Bhattacharjee, Arnab Mondal, Arnab Kumar Verma, Ashu Mishra, Sukumar Saha, Tapan K. Univ Queensland IIT Delhi Acad Res Dept Elect Engn St Lucia Qld 4067 Australia Indian Inst Technol Delhi Amar Nath & Shashi Khosla Sch Informat Technol New Delhi 110016 India Indian Inst Technol Delhi Dept Energy Sci & Engn New Delhi 110016 India Indian Inst Technol Delhi Elect Engn Dept New Delhi 110016 India Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

This paper proposes a self-supervised latent space clustering algorithm, called the Deep Latent Space clustering, for the detection of stealthy false data injection attacks (FDIAs) in smart grids against state estimation algorithms. The stealthy FDI model considered is based on the accurate AC state estimation and is able to bypass conventional bad data detection (BDD) algorithms with ease. The key element of the detection model is a stacked autoencoder network that first undergoes a carefully designed two-step finetuning process, following which a trainable clustering head is stacked on top of the finetuned encoder and the final network is further trained to achieve a clean clustering of the data into benign and compromised samples without labelled supervision. To test the efficacy and scalability of the detection model, it is tested on the standard IEEE 14 bus, 118 bus and 300 bus test systems. The self-supervised clustering model is compared to several supervised, semi-supervised and unsupervised algorithms proposed in the literature for detection of FDI on the aforementioned test cases and has been found to perform at par with the state of the art among them.

关键词： Cyberattack Deep learning clustering algorithms State estimation Encoding Self-supervised learning Cyberattack detection stealthy false data injection attacks autoencoders latent space clustering AC state estimation

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