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End-to-End Multiview Fuzzy clustering With Double Representation Learning and Visible-Hidden View Cooperation

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第2期32卷 483-497页

作者： Yang, Hongtan Deng, Zhaohong Zhang, Wei Wu, Qunzhuo Choi, Kup-Sze Wang, Shitong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Jiangsu Key Lab Media Design & Software Technol Wuxi 214122 Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

Multiview clustering has received great attention in recent years for the potential in clustering performance improvement by using cooperative learning of different views. Despite the considerable progress, a few issues remain: 1) real multiview data contains redundant features and noises that lead to unsatisfactory clustering performance;2) most existing multiview clustering methods only mine the shared information between views and ignore the specific information within views;and 3) most multiview clustering methods are based on a two-step framework that learn the hidden view representation and then perform clustering, overlooking the correlation between the two processes. Although some approaches have been proposed to deal with these issues, they cannot them simultaneously. To this end, we propose an end-to-end multiview fuzzy clustering. First, we construct a multiview fuzzy clustering framework to mine the specific information of the visible views. Second, to reduce the impact of redundant features and noises on clustering performance, we introduce the orthogonal projection matrix into the clustering framework to learn the low-dimensional representation of the visible views. Meanwhile, this procedure is integrated into the clustering framework. Third, we explore the shared hidden view representation between the visible views by multiview non-negative matrix factorization and integrate it into the clustering framework to realize visible-hidden view cooperation learning. Finally, the shared hidden view representation learning between visible views, the low-dimensional representation learning of visible views, and the clustering partition of multiview data negotiate with each other in the end-to-end learning framework. Extensive experiments on benchmark multiview datasets indicate the superiority of the proposed method over state-of-the-art methods.

关键词： clustering methods Representation learning clustering algorithms Partitioning algorithms Linear programming Correlation Loss measurement Fuzzy clustering multiview learning visible-hidden cooperation learning representation learning

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Heterogeneous density-based clustering with a dual-functional memristive array

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MATERIALS HORIZONS 2024年第18期11卷 4493-4506页

作者： Shin, Dong Hoon Cheong, Sunwoo Lee, Soo Hyung Jang, Yoon Ho Park, Taegyun Han, Janguk Shim, Sung Keun Kim, Yeong Rok Han, Joon-Kyu Baek, In Kyung Ghenzi, Nestor Hwang, Cheol Seong Seoul Natl Univ Dept Mat Sci & Engn Seoul 08826 South Korea Seoul Natl Univ Coll Engn Inter Univ Semicond Res Ctr Seoul 08826 South Korea Univ Avellaneda UNDAV Mario Bravo 1460 RA-1872 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Conicet Mario Bravo 1460 RA-1460 Buenos Aires DF Argentina Sogang Univ Syst Semicond Engn & Dept Elect Engn Seoul South Korea

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems. A reconfigurable dual-mode memristive array was utilized for the proposed heterogeneous density-based clustering. Both digital and analog modes were analyzed, and the algorithm was verified through vector-matrix multiplication in both modes.

关键词： clustering algorithms

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Incomplete Multiple Kernel Alignment Maximization for clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第3期46卷 1412-1424页

作者： Liu, Xinwang Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

Multiple kernel alignment (MKA) maximization criterion has been widely applied into multiple kernel clustering (MKC) and many variants have been recently developed. Though demonstrating superior clustering performance in various applications, it is observed that none of them can effectively handle incomplete MKC, where parts or all of the pre-specified base kernel matrices are incomplete. To address this issue, we propose to integrate the imputation of incomplete kernel matrices and MKA maximization for clustering into a unified learning framework. The clustering of MKA maximization guides the imputation of incomplete kernel elements, and the completed kernel matrices are in turn combined to conduct the subsequent MKC. These two procedures are alternately performed until convergence. By this way, the imputation and MKC processes are seamlessly connected, with the aim to achieve better clustering performance. Besides theoretically analyzing the clustering generalization error bound, we empirically evaluate the clustering performance on several multiple kernel learning (MKL) benchmark datasets, and the results indicate the superiority of our algorithm over existing state-of-the-art counterparts.

关键词： Kernel clustering algorithms Optimization Task analysis Pattern analysis Partitioning algorithms Minimization Multiple kernel clustering multi-view clustering kernel alignment maximization

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A Fast Hybrid Feature Selection Method Based on Dynamic clustering and Improved Particle Swarm Optimization for High-Dimensional Health Care Data

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 2447-2459页

作者： Kang, Yan Peng, Luhan Guo, Jing Lu, Yuhuan Yang, Yun Fan, Baochen Pu, Bin Yunnan Univ Natl Pilot Sch Software Yunnan Key Lab Software Engn Kunming 650106 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

The ubiquity and commoditization of wearable sensors have generated a deluge of user-generated health care data and played a key role in clinical utility, particularly when incorporated into personalized prediction models. The "curse of dimensionality" and enormous computational costs are still the main challenges faced by the existing algorithms as the number of wearable datasets exponentially increases. We propose a novel method by hybridizing a clustering method and a wrapper method to reduce the dimensionality of raw wearable datasets while preserving health care information. In the clustering stage, a dynamic correlation-guided feature clustering method reduces the search space by designing a dynamic threshold to filter unrelated high-dimensional features. In the wrapper stage, we obtain the optimal feature subset by improving the powerful search capability of the particle swarm optimization algorithm. A crossover operator based on normalized mutual information similarity is proposed to match particles, which effectively improves the diversity of the offspring swarm to prevent premature convergence. In addition, we propose a dynamic swarm strategy to mutate the duplicate particles in the swarm to enhance the efficiency of the particle search process. Our method is evaluated on ten real public datasets, and the experimental results demonstrate its superior performance.

关键词： Heuristic algorithms clustering algorithms Feature extraction Medical services Filtering algorithms Classification algorithms Biomedical monitoring Feature selection health care data high-dimensional data correlation-guided clustering particle swarm optimization

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Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved clustering Algorithm

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IEEE ACCESS 2024年 12卷 76634-76647页

作者： Zhang, Ning Zhu, Liang Heilongjiang Univ Technol Sch Elect & Informat Engn Jixi 158100 Peoples R China

With the application and development of new technologies such as the Internet of Things and big data in the energy field, smart grids have gradually become the main trend for the future development of the power system. However, frequent communication between various participants in the smart grid can lead to higher privacy leakage risks, and malicious attackers can cause economic losses and security accidents to the smart grid by injecting abnormal power data. To address the issues of data privacy protection and anomaly detection in smart grids, this study proposed a matrix completion based smart grid data privacy protection scheme and an unsupervised learning based smart grid anomaly data detection scheme. A matrix completion based smart grid data privacy protection scheme was proposed to address the issue of data privacy protection between different trust domains in the smart grid. This scheme repaired missing elements in the relevant matrix through matrix completion, and increased random perturbations by adding noise with the same statistical characteristics as the original data to achieve data privacy protection. To address the issue of missing power data labels and inaccurate principal component analysis caused by abnormal data, an improved K-means clustering algorithm was used to remove outliers and optimize the results, to achieve accurate detection of abnormal data. The experimental results showed that the proposed algorithm has an accuracy of 0.9, an F1 value of 0.7, and a Bayesian detection rate of 0.61, all of which are higher than other algorithms. The AUC value of the proposed method was 0.79, indicating that the scheme established in this chapter has certain advantages in detecting abnormal electricity consumption.

关键词： Smart grids Electricity Data privacy Protection clustering algorithms Security Quantum computing Internet of Things Matrix completion K-means clustering algorithm smart grid data privacy monitor

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Sparse Membership Affinity Lasso for Fuzzy clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第3期32卷 1553-1563页

作者： Huang, Junjun Lu, Xiliang Yang, Jerry Zhijian Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China YGSoft Inc Zhuhai 519085 Peoples R China Wuhan Univ Hubei Key Lab Computat Sci Wuhan 430072 Peoples R China

The membership matrix is a key element in fuzzy clustering, enabling novel data representation in multiple clusters. The row vectors of the membership matrix represent each sample's degree of membership to different clusters. Notably, researchers have confirmed the presence of the local affinity among these row vectors, effectively preserving the local structure of the original data distribution. However, in this work, we consider that most sample points have insignificant fuzziness, with fuzziness found primarily in a few clusters, resulting in most membership vectors being sparse. To tackle this issue, we present the sparse, membership-affinity fuzzy clustering model, which leverages the sparsity of the row vectors and its affinity to establish a more appropriate representation, along with an optimization algorithm. Our experimental results on both simulated and real datasets demonstrate that the combination of sparsity and affinity can significantly enhance fuzzy clustering performance over other models.

关键词： clustering algorithms Entropy Geometry Encoding Sparse matrices Robustness Linear programming Fuzzy clustering membership affinity lasso membership sparsity

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SA-PSO-GK plus plus : A New Hybrid clustering Approach for Analyzing Medical Data

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IEEE ACCESS 2024年 12卷 12501-12516页

作者： Abdo, Amani Abdelkader, Omnia Abdel-Hamid, Laila Arab Open Univ Fac Comp Cairo 11211 Egypt Helwan Univ HU Fac Comp & Artificial Intelligence Cairo 11795 Egypt

Data clustering is an unsupervised learning task that has been extensively studied, given its wide applicability in various domains. Traditional algorithms often struggle to achieve a balance between exploration and exploitation, leading to sub-optimal solutions. This paper presents a novel hybrid algorithm named SA-PSO-GK++ that synergistically combines Particle Swarm Optimization (PSO), K-means++, Simulated Annealing (SA), and Gaussian Estimation of Distribution to tackle this issue effectively. The proposed SA-PSO-GK++ aims to overcome the drawbacks of existing methods by leveraging the strengths of each individual algorithm. The K-means++ initialization reduces the risk of poor initial centroids, while PSO aids in efficient search space exploration. GED provides a statistical model of the particle space, enabling the algorithm to generate new potential solutions that are statistically guided by the current best solutions. Additionally, the incorporation of Simulated Annealing allows the algorithm to escape local minima, thereby enhancing its global search capability. We evaluate the effectiveness of SA-PSO-GK++ using benchmark datasets from the UCI Machine Learning Repository, including the Iris, Breast cancer, Heart datasets and contraceptive method choice datasets. The proposed method outperforms conventional and some of the state-of-the-art hybrid clustering algorithms in terms of sum of euclidean distance, normalized index, and error rates. These advantages make SA-PSO-GK++ a compelling option for a wide range of clustering applications. The results offer promising avenues for future research in optimizing and applying this innovative clustering technique in diverse domains.

关键词： clustering algorithms Particle swarm optimization Convergence Estimation Simulated annealing Optimization Robustness Swarm intelligence particle swarm optimization (PSO) K-means K-means plus plus simulated annealing Gaussian estimation data clustering big data cluster convergence clustering metrics local optima

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Interrelate Training and clustering for Online Speaker Diarization

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2024年 32卷 1352-1364页

作者： Chen, Yifan Cheng, Gaofeng Yang, Runyan Zhang, Pengyuan Yan, Yonghong Chinese Acad Sci Inst Acoust Key Lab Speech Acoust & Content Understanding Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In clustering-based speaker diarization systems, the embedding clusters for distinctive speakers exhibit wide variability in size and density, posing difficulty for clustering accuracy. In spite of this, with the assistance of the overall distance relationships among speaker embeddings, most of the embeddings can be grouped to the correct cluster by sophisticated offline clustering algorithms. However, in online scenarios, such a complete distance relationships of the embeddings can not be obtained due to the incremental arrival of embeddings. Consequently, determining the number of clusters and then correctly grouping the embeddings become challenging in an online fashion. Furthermore, errors would accumulate quickly over time if the online clustering algorithm assigns the embeddings into clusters erroneously in the beginning. To address these problems, we designed a novel framework for online clustering. To reduce the high variability of speaker embeddings, we proposed the clustering guided embedding extractor training (CGEET) algorithm to encourage similarity between the size of the embedding space for different speakers in attempt to simplify the distance relationships of embeddings. The CGEET algorithm can grasp the distance information of the entire speaker embedding space and provide it to the online clustering algorithm. With this preliminary information, the distance thresholds guided online clustering (DTGOC) algorithm then processes incoming embeddings using a divide-and-conquer approach. It first handles the embeddings with explicit distance relationships and then searches for possible path combination they have with remaining embeddings in an online fashion. Moreover, in order to utilize the distance relationships of embeddings that are far apart in time, an online re-clustering strategy is incorporated in our DTGOC algorithm, which can alleviate error accumulation during online clustering. By implementing the above innovations, our proposed online clus

关键词： clustering algorithms Training Task analysis Oral communication Speech processing Acoustics Machine learning algorithms Metric embedding learning online clustering speaker diarization

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A Novel Fuzzy clustering Method Based on Topological Connected Component

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第3期32卷 1050-1062页

作者： Zheng, Bowen Ma, Liwen Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Key Lab Math & Informat Networks Minist Educ Beijing 100876 Peoples R China

With the development of fuzzy set theory, the clustering methods widely used in various fields can be divided into hard clustering methods and fuzzy clustering methods. Fuzzy clustering methods use the membership function with values in the interval [0, 1] to describe fuzzy concepts, which is different from the idea of "non-0 is 1" in hard clustering methods, so they are more suitable for some real datasets. In this article, we introduce a novel fuzzy clustering method called fuzzy connected clustering (FCC), which is based on the idea of topological connected components. According to the basic view of topology, FCC directly considers the location characteristics between objects, unlike the common clustering methods that need artificial conditions, such as distance scale, cluster center, fuzzy similar matrix, etc. We use topological knowledge to derive FCC through a series of strict logical reasoning, and design a new algorithm, FCC algorithm. Furthermore, we propose a method of constructing "partial order" to express the clustering results of FCC intuitively. Combining with the application examples of FCC, we not only demonstrate the FCC's high interpretability in dealing with real-world problems, but also compare FCC with the commonly used fuzzy clustering method FEMCM. Moreover, we also try to use a benchmark dataset to show how well the FCC deals with real problems related to fuzzy concepts. Finally, we further discuss the extensions and limitations of FCC, and give some basic directions to effectively address these limitations, as well as prospects for future work related to this novel clustering method FCC.

关键词： clustering methods FCC clustering algorithms Topology Telecommunications Linear programming Fuzzy set theory Connected component fuzzy clustering fuzzy component clustering (FCC) partial order topology

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Deep Fuzzy clustering Network With Matrix Norm Regularization

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IEEE ACCESS 2024年 12卷 28677-28683页

作者： Chen, Feiyu Li, Yan Wang, Wei Chongqing Normal Univ Natl Ctr Appl Math Chongqing Chongqing 401331 Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China Army Med Univ Dept Comp Sci Chongqing 400038 Peoples R China

Recently, deep clustering networks, which able to learn latent embedding and clustering assignment simultaneously, attract lots of attention. Among the deep clustering networks, the suitable regularization term is not only beneficial to training of neural network, but also enhancing clustering performance. In the paper, we propose a deep fuzzy clustering network with mixed matrix norm regularization (DFCNR). Specifically, DFCNR uses the weighted intra-class variance as clustering loss, l(1,2) norm and the Frobenius norm of soft assignment matrix as regularization term, where the minimization of l(1,2) norm aims to achieve balanced assignment, and maximization of Frobenius norm aims to achieve discriminative assignment. Moreover, by solving the quadratic convex constraint optimization problem about soft assignment, we derive the activation function of clustering layer. Extensive experiments conducted on several datasets illustrate the superiority of the proposed approach in comparison with current methods.

关键词： clustering methods Training Representation learning clustering algorithms Linear programming Vectors Neural networks Encoding Deep learning Fuzzy systems Autoencoder deep fuzzy clustering deep learning matrix norm regularization

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