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Comparative Study on Noise-Estimation-Based Fuzzy C-Means clustering for Image Segmentation

IEEE TRANSACTIONS ON CYBERNETICS 2024年第1期54卷 241-253页

作者： Wang, Cong Zhou, MengChu Pedrycz, Witold Li, ZhiWu Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Since a noisy image has inferior characteristics, the direct use of Fuzzy C-Means (FCM) to segment it often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM's robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task. To date, only two noise-estimation-based FCM algorithms have been proposed for image segmentation, that is: 1) deviation-sparse FCM (DSFCM) and 2) our earlier proposed residual-driven FCM (RFCM). In this article, we make a thorough comparative study of DSFCM and RFCM. We demonstrate that an RFCM framework can realize more accurate noise estimation than DSFCM when different types of noise are involved. It is mainly thanks to its utilization of noise distribution characteristics instead of noise sparsity used in DSFCM. We show that DSFCM is a particular case of RFCM, thus signifying that they are the same when only impulse noise is involved. With a spatial information constraint, we demonstrate RFCM's superior effectiveness and efficiency over DSFCM in terms of supporting experiments with different levels of single, mixed, and unknown noise.

关键词： Image segmentation Estimation Robustness Kernel clustering algorithms Task analysis Noise measurement Comparative study distribution characteristic Fuzzy C-Means (FCM) image segmentation noise estimation

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Balanced Fair K-Means clustering

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 5914-5923页

作者： Pan, Renbo Zhong, Caiming Qian, Jiangbo Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Ningbo Univ Coll Sci & Technol Ningbo 315211 Peoples R China

Fairness in clustering has recently received significant attention. The goal of fair clustering is to ensure that a clustering algorithm mitigates or even eliminates bias in the original dataset. Many existing fair clustering algorithms will sometimes generate numerous small clusters to satisfy the fairness constraint. In this article, we present a balanced fair K-means clustering algorithm that integrates a fairness constraint and a balance constraint into the K-means objective function. The proposed model is a tradeoff between the K-means objective and the fairness constraint and their relative importance can be controlled. The balance constraint prevents the generation of small clusters. Experimental results on both real-world and synthetic datasets demonstrate that the proposed method achieves a better fairness performance than some other fair clustering methods, with an acceptable loss of clustering quality in some cases and an improvement in others.

关键词： clustering algorithms Machine learning algorithms Machine learning Informatics Upper bound Software Optimization Cluster size balance fair clustering fairness K-means clustering

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Adaptively Topological Tensor Network for Multi-View Subspace clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 5562-5575页

作者： Liu, Yipeng Chen, Jie Lu, Yingcong Ou, Weiting Long, Zhen Zhu, Ce Univ Elect Sci & Technol China UESTC Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Multi-view subspace clustering employs learned self-representation from multiple tensor decompositions to exploit the low-rank information. However, the data structures embedded with self-representation tensors may vary in different multi-view datasets. Therefore, a pre-defined decomposition may not fully exploit low-rank information from various data, resulting in sub-optimal multi-view clustering performance. To alleviate this, we proposed the adaptively topological tensor network (ATTN). ATTN can learn a suitable decomposition structure that can represent the low-rank structure and high-order correlation of the self-representation tensors better in a data-driven way, which can capture the intra-view and inter-view information better. Firstly, instead of connecting the tensor network blindly, ATTN utilizes the correlation between adjacent factors to prune redundant connections from the fully connected tensor network, making the tensor network more expressive. Furthermore, a greedy adaptive rank-increasing strategy is applied to optimize the pruned tensor network structure, which improves the capacity of capturing low-rank structure. We apply ATTN on a multi-view subspace clustering task and utilize the alternating direction method of multipliers(ADMM) method to optimize it. Experiments show that multi-view subspace clustering based on ATTN has better performance on nine multi-view datasets.

关键词： Tensors Correlation clustering algorithms Image reconstruction Task analysis Indexes Color Subspace clustering tensor network architecture tensor decomposition low-rank representation

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Classification of synoptic circulation patterns with a two-stage clustering algorithm using the modified structural similarity index metric (SSIM)

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EARTH SYSTEM DYNAMICS 2024年第3期15卷 607-633页

作者： Winderlich, Kristina Dalelane, Clementine Walter, Andreas Deutsch Wetterdienst Climate & Environm Consultancy Dept German Meteorol Serv D-63067 Offenbach Germany

We develop a new classification method for synoptic circulation patterns with the aim to extend the evaluation routine for climate simulations. This classification is applicable to any region of the globe of any size given the reference data. Its unique novelty is the use of the modified structural similarity index metric (SSIM) instead of traditional distance metrics for cluster building. This classification method combines two classical clustering algorithms used iteratively, hierarchical agglomerative clustering (HAC) and k -medoids, with only one pre-set parameter - the threshold on the similarity between two synoptic patterns expressed as the structural similarity index measure (SSIM). This threshold is set by the user to imitate the human perception of the similarity between two images (similar structure, luminance, and contrast), whereby the number of final classes is defined *** apply the SSIM-based classification method to the geopotential height at the pressure level of 500 hPa from the ERA-Interim reanalysis data for 1979-2018 and demonstrate that the built classes are (1) consistent with the changes in the input parameter, (2) well-separated, (3) spatially stable, (4) temporally stable, and (5) physically *** demonstrate an exemplary application of the synoptic circulation classes obtained with the new classification method for evaluating Coupled Model Intercomparison Project Phase 6 (CMIP6) historical climate simulations and an alternative reanalysis (for comparison purposes): output fields of CMIP6 simulations (and of the alternative reanalysis) are assigned to the classes and the Jensen-Shannon distance is computed for the match in frequency, transition, and duration probabilities of these classes. We propose using this distance metric to supplement a set of commonly used metrics for model evaluation.

关键词： clustering algorithms

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Soft Multiprototype clustering Algorithm via Two-Layer Semi-NMF

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 1615-1629页

作者： Zeng, Shan Duan, Xiangjun Bai, Jun Tao, Wei Hu, Kun Tang, Yuanyan Wuhan Polytech Univ Sch Math & Comp Sci Wuhan 430048 Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic 3125 Australia Univ Sydney Sch Comp Sci Camperdown NSW 2050 Australia Univ Macau Fac Sci & Technol Macau 999078 Peoples R China

This article proposes a novel soft multiprototype clustering algorithm (SMP) for high-dimensional data clustering with noisy and complex structural patterns. SMP integrates dimensionality reduction, multiprototype clustering, and multiprototype merge clustering under a two-layer seminonnegative matrix factorization (semi-NMF) architecture. Specifically, the first semi-NMF layer performs multiprototype clustering, which solves the problem that a single prototype cannot represent complex data structures. Meanwhile, the multiprototype fuzzy clustering constraints ensure that the multiprototypes better characterize the original data structure. The second semi-NMF layer performs multiprototype merge clustering to mitigate the issues of heavy computation burden and poor antinoise performance of the spectral clustering algorithm. The introduction of the Laplace graph matrix regularization constraint in this layer assists SMP in completing the merging of multiprototypes with complex data structures. Comprehensive experiments demonstrate that the proposed method outperforms the state-of-the-art algorithms.

关键词： clustering algorithms Dimensionality reduction Kernel Prototypes Switched mode power supplies Partitioning algorithms Merging Fuzzy clustering Laplace graph regularization seminonnegative matrix factorization (semi-NMF) soft multiprototype clustering

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Temporal Multi-Features Representation Learning-Based clustering for Time-Series Data

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IEEE ACCESS 2024年 12卷 87675-87690页

作者： Lee, Jaehoon Kim, Dohee Sim, Sunghyun Dong Eui Univ Dept Math Informat Stat Busan 47340 South Korea Pusan Natl Univ Safe & Clean Supply Chain Res Ctr Busan 46241 South Korea Dong Eui Univ Dept Ind Management & Big Data Engn Busan 47340 South Korea

Time-series clustering remains a challenge in data mining. Although novel deep-learning-based representation learning integrated with deep clustering methods have considerably enhanced the performance of time-series clustering, efficiently capturing the various temporal patterns inherent in the data is difficult in representation learning for time-series data. In this study, we proposed a novel representation learning method called temporal multi-features representation learning (TMRL) to capture various temporal patterns embedded in time-series data. Based on TMRL, we introduce the temporal multi-features representation clustering (TMRC) framework for performing time-series clustering. The proposed framework decomposes the input time-series data into k temporal patterns and uses k LSTM autoencoders to extract specialized features for each decomposed diverse temporal pattern through TMRL. Variational-mode decomposition is used to extract temporal multi-features. Finally, temporal multi-features derived from TMRL are ensembled for time-series clustering. To evaluate the superiority of the proposed method, comparative experiments were conducted with 36 publicly available time-series datasets against 16 baseline models. In the comparative experiments, we achieved the highest RI and normalized mutual information values in 12 time-series datasets. Particularly, on datasets consisting of eight types of motion- and spectro-types, the proposed method attained the highest RI and NMI values in six datasets. Furthermore, visualization results of the learned features through TMRL demonstrated superior representation learning compared with existing methods. These results indicated that the proposed TMRC framework is highly suitable for the learning representations of time-series data and can be effectively used for time-series clustering.

关键词： Feature extraction Representation learning clustering methods Data mining clustering algorithms Time series analysis Distortion measurement Time-series clustering temporal multi-features representation (TMRC) temporal multi-features representation learning (TMRL) variational mode decomposition (VMD)

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Extraction of Representative Scenarios for Photovoltaic Power With Shared Weight Graph clustering

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IEEE TRANSACTIONS ON SMART GRID 2024年第6期15卷 6158-6170页

作者： Fu, Xueqian Lu, Na Sun, Hongbin Zhang, Youmin China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China China Agr Univ Natl Innovat Ctr Digital Fishery Beijing 100083 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada

With the growing integration of photovoltaic (PV) generation, the operational conditions of power systems become more complex and variable. These intricate scenarios place significant pressure on the optimization calculations for power systems, necessitating the extraction of representative scenarios for PV power generation to enhance optimization efficiency. To address this issue, we have proposed a novel clustering model that extracts representative PV output scenarios through the fusion of adaptive feature weights and adjacent density weights. We propose an alternating optimization solution algorithm based on the Lagrange multiplier method and eigenvalue decomposition. The highlight of this work is the dual verification through theoretical proof and simulation experiment. In terms of theoretical proof, we analyze the sensitivity of clustering model parameters, demonstrate algorithm complexity, and theoretically prove the convergence of the proposed solution algorithm. Using actual PV output data from Australia, we validate the high cohesion, low coupling, noise resistance, and parameter sensitivity of the proposed clustering model, as well as the convergence of the proposed solution algorithm. The effectiveness of the proposed method in extracting representative scenarios of PV output has been confirmed through probabilistic power flow analysis using two IEEE test cases.

关键词： Power generation clustering algorithms Optimization Planning Power systems Feature extraction Uncertainty Photovoltaic representative scenario active distribution network probabilistic power flow clustering algorithm

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Local-Global Fuzzy clustering With Anchor Graph

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第1期32卷 188-202页

作者： Wang, Jingyu Guo, Shengzhao Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

Recently, anchor-based strategy is getting a lot of attention, which extends spectral clustering to reveal the dual relation between samples and features. However, the acquisition of clustering results follows the relaxed-discrete procedure, which might lead to serious information loss. Given that, local-global fuzzy clustering with anchor graph is proposed in this article, which jointly completes subspace learning and clustering. With the construction of anchor graph, we first impose the fuzzy constraint on the indicator matrix, which is beneficial for revealing the ambiguity and uncertainty in clustering tasks. Thereafter, we introduce the graph divergence regularization term for maximizing the variance of fuzzy indicator matrix, which not only avoids the trivial solutions in local graph learning, but also enhances the separability of data for explicit global structure. In this way, the local graph loss and global divergence regularization term are able to jointly capture the critical clustering structure. Finally, we can obtain the clustering results in accordance with the optimal fuzzy indicator matrix, which is updated alternately by the presented coordinate descent method in optimization process. Therefore, the desirable discrete labels come out automatically under the fuzzy strategy without extra discretization operations, which conforms to reality. The effectiveness of our method will be demonstrated through comprehensive experimental results.

关键词： Eigenvalues and eigenfunctions Task analysis Optimization clustering methods Laplace equations clustering algorithms Visualization Anchor-based graph fuzzy clustering graph divergence regularization term local-global structure trivial solutions

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An End-to-End Approach for Graph-Based Multi-View Data clustering

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IEEE TRANSACTIONS ON BIG DATA 2024年第5期10卷 644-654页

作者： Dornaika, Fadi El Hajjar, Sally Univ Basque Country UPV EHU San Sebastian 20018 Spain Ho Chi Minh City Open Univ Ho Chi Minh City 700000 Vietnam

clustering data from different sources or views is a key challenge in real-world applications. While traditional graph-based methods are effective at capturing data structures, they often require separate steps to estimate graphs of views or a consensus graph from the raw data. This reliance on intermediate steps can make these clustering methods susceptible to noisy graphs, which affects the overall performance of clustering. In response to this limitation, and with an emphasis on advocating end-to-end solutions for multi-view clustering, two comprehensive approaches are presented in this paper. Each approach starts from either the raw data or its kernelized features. The first proposal introduces a unified objective function that enables the simultaneous recovery of the graph for each view, the unified graph, the spectral projection matrices for all views, the soft cluster assignments, and the scores assigned to each view. The second proposal uses a global criterion that integrates regularization and constraints for the soft cluster assignment matrix based on the consensus graph matrix and the consensus data representation. Both proposed methods enable direct and straightforward clustering of the data without the need for additional steps. Extensive tests with various real-world image and text datasets confirm the superior performance of the two proposed methods.

关键词： clustering algorithms Vectors Big Data Smoothing methods Proposals Linear programming Convolution Graph-based multi-view clustering unified graph learning data fusion spectral embedding soft cluster assignments

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Large Language Models Enable Few-Shot clustering

TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTIC...

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TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS 2024年 12卷 321-333页

作者： Viswanathan, Vijay Gashteovski, Kiril Lawrence, Carolin Wu, Tongshuang Neubig, Graham Carnegie Mellon Univ Pittsburgh PA 15213 USA NEC Labs Europe Heidelberg Germany Ss Cyril & Methodius Univ Skopje Ctr Adv Interdisciplinary Res Skopje North Macedonia

Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the user's intent. Existing approaches to semi-supervised clustering require a significant amount of feedback from an expert to improve the clusters. In this paper, we ask whether a large language model (LLM) can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering. We show that LLMs are surprisingly effective at improving clustering. We explore three stages where LLMs can be incorporated into clustering: before clustering (improving input features), during clustering (by providing constraints to the clusterer), and after clustering (using LLMs post-correction). We find that incorporating LLMs in the first two stages routinely provides significant improvements in cluster quality, and that LLMs enable a user to make trade-offs between cost and accuracy to produce desired clusters. We release our code and LLM prompts for the public to use.1

关键词： clustering algorithms

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