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Late Fusion Multiple Kernel clustering With Local Kernel Alignment Maximization

IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 993-1007页

作者： Zhang, Tiejian Liu, Xinwang Gong, Lei Wang, Siwei Niu, Xin Shen, Li Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

Multi-view clustering, which appropriately integrates information from multiple sources to reveal data's inherent structure, is gaining traction in clustering. Though existing procedures have yielded satisfactory results, we observe that they have neglected the inherent local structure in the base kernels. This may cause adverse effects on clustering. To solve the problem, we introduce LF-MKC-LKA, a simple yet effective late fusion multiple kernel clustering with local kernel alignment maximisation approach. In particular, we first determine the nearest $k$ neighbours in the average kernel space for each sample and record the information in the nearest neighbor indicator matrix. Then, the nearest neighbor indicator matrix can be used to generate local structure matrix of each sample. The local kernels of each view may then be generated using the local structure matrix, retaining just the highly confident local similarities for learning the intrinsic global manifold of data. They can also be utilised to keep the block diagonal structure and improve the robustness of the underlying kernels against *** input the local kernels of each view into the kernel $k$-means (KKM) algorithm and get the local base partitions. Finally, we use a three-step iterative optimization approach to maximize the alignment of the consensus partition using base partitions and a regularisation term. As demonstrated, a significant number of trials on 11 multi-kernel benchmark datasets have shown that the proposed LF-MKC-LKA is effective and efficient. A number of experiments are also designed to demonstrate the fast convergence, excellent performance, robustness and low parameter sensitivity of the algorithm. Our code can be find at https://***/TiejianZhang/TMM21-LF-MKC-LKA.

关键词： Kernel clustering algorithms Robustness Partitioning algorithms Optimization Manifolds Benchmark testing Multiple kernel clustering neighbor local kernel local base partition block diagonal structure

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Workflow for mitigating shape diversity in free-form surface panels through clustering and evaluation

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2025年

作者： Liu, Hequn Liu, Yan Southeast Univ Sch Architecture 2 Sipailou Nanjing 210018 Peoples R China Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

With the advancement of digital technologies, free-form surfaces are increasingly utilized in architectural design. Constructing such surfaces typically involves dividing them into numerous panels, which are often uniquely shaped, thereby significantly increasing fabrication and construction complexity, as well as adding to the economic burden. This paper introduces a workflow for optimizing free-form surface facades by reducing panel shape diversity. The proposed workflow consists of four key components: similarity measures (Euclidean and Manhattan distances), clustering algorithms (hierarchical clustering, K-means, and HDBSCAN), evaluation metrics (SC, DB, and CH), and validation through three case studies. The study confirms the correlation between clustering algorithm performance and surface type, highlighting its dependence on input data characteristics. The workflow is applied to the panel optimization of the Hongdao Railway Station project, validating its practical applicability by focusing on the selection of optimal algorithms and the configuration of clustering parameters. As a modular approach, it establishes a foundation for further refinement and extension of methods in future applications.

关键词： clustering algorithms validation metrics shape similarity assessment free-form surface

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Multi-View Discrete clustering: A Concise Model

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 15154-15170页

作者： Qiang, Qianyao Zhang, Bin Wang, Fei Nie, Feiping Xi An Jiao Tong Univ Sch Software Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China

In most existing graph-based multi-view clustering methods, the eigen-decomposition of the graph Laplacian matrix followed by a post-processing step is a standard configuration to obtain the target discrete cluster indicator matrix. However, we can naturally realize that the results obtained by the two-stage process will deviate from that obtained by directly solving the primal clustering problem. In addition, it is essential to properly integrate the information from different views for the enhancement of the performance of multi-view clustering. To this end, we propose a concise model referred to as Multi-view Discrete clustering (MDC), aiming at directly solving the primal problem of multi-view graph clustering. We automatically weigh the view-specific similarity matrix, and the discrete indicator matrix is directly obtained by performing clustering on the aggregated similarity matrix without any post-processing to best serve graph clustering. More importantly, our model does not introduce an additive, nor does it has any hyper-parameters to be tuned. An efficient optimization algorithm is designed to solve the resultant objective problem. Extensive experimental results on both synthetic and real benchmark datasets verify the superiority of the proposed model.

关键词： Laplace equations clustering algorithms Optimization Symmetric matrices Benchmark testing Analytical models Additives Multi-view clustering graph clustering discrete indicator matrix

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Energy Efficient Wireless Crowd Labeling: Joint Annotator clustering and Power Control

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第3期22卷 2022-2035页

作者： Li, Xiaoyang Zhu, Guangxu Shen, Kaiming Han, Kaifeng Huang, Kaibin Gong, Yi Southern Univ Sci & Technol Dept Elect & Elect Engn EEE Shenzhen 518055 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518115 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen 518172 Peoples R China China Acad Informat & Commun Technol Beijing 100045 Peoples R China Univ Hong Kong Dept EEE Hong Kong Peoples R China

The unprecedented growth of mobile data traffic has fueled the deployment of artificial intelligence (AI) at the network edge, while distilling the intelligence from raw data by machine learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast via fading channels to different clusters of annotators for repetition labelling to improve the accuracy. Given the desired labelling accuracy, the superposition coding technique together with the repetition labelling scheme give rise to a new tradeoff between radio-and-annotator resource consumption. Building on such tradeoff, the annotator clustering and transmit power control are jointly optimized to maximize the labelling throughput (i.e., the number of labelled objects) or minimize the power consumption, resulting in NP-hard integer programming problems. To solve these problems, the optimal structure of annotator clustering is derived by exploiting the property that the power allocation for multicasting objects tends to compensate for the worst channel among the annotators in each cluster. Based on such structure, the throughput maximization problem can be recognized as a longest-path problem and solved by means of branch-and-bound, while the power minimization problem can be recasted to a shortest-path problem and solved by means of forward dynamic programming. The solution approaches can be further simplified when the channels are symmetric by merging the same nodes and cutting the identical paths in the path graph. In addition, exact polices are derived for the special cases where either the annotators or power are constrained. Last, simulation results are presented to demonstrate the performance of our proposed joint designs.

关键词： Labeling Wireless communication Resource management Throughput Power control clustering algorithms Servers Crowd labelling clustering power control energy efficiency artificial intelligence

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SMMP: A Stable-Membership-Based Auto-Tuning Multi-Peak clustering Algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第5期45卷 6307-6319页

作者： Guan, Junyi Li, Sheng He, Xiongxiong Zhu, Jinhui Chen, Jiajia Si, Peng Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Zhejiang Univ Affiliated Hosp 2 Sch Med Hangzhou 310014 Peoples R China

Since most existing single-prototype clustering algorithms are unsuitable for complex-shaped clusters, many multi-prototype clustering algorithms have been proposed. Nevertheless, the automatic estimation of the number of clusters and the detection of complex shapes are still challenging, and to solve such problems usually relies on user-specified parameters and may be prohibitively time-consuming. Herein, a stable-membership-based auto-tuning multi-peak clustering algorithm (SMMP) is proposed, which can achieve fast, automatic, and effective multi-prototype clustering without iteration. A dynamic association-transfer method is designed to learn the representativeness of points to sub-cluster centers during the generation of sub-clusters by applying the density peak clustering technique. According to the learned representativeness, a border-link-based connectivity measure is used to achieve high-fidelity similarity evaluation of sub-clusters. Meanwhile, based on the assumption that a reasonable clustering should have a relatively stable membership state upon the change of clustering thresholds, SMMP can automatically identify the number of sub-clusters and clusters, respectively. Also, SMMP is designed for large datasets. Experimental results on both synthetic and real datasets demonstrated the effectiveness of SMMP.

关键词： clustering algorithms Shape Density measurement clustering methods Resource management Periodic structures Time complexity clustering density peak arbitrary shape clustering auto-tuning

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ECCA: Efficient Correntropy-Based clustering Algorithm With Orthogonal Concept Factorization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第10期34卷 7377-7390页

作者： Yang, Ben Zhang, Xuetao Nie, Feiping Chen, Badong Wang, Fei Nan, Zhixiong Zheng, Nanning Xi An Jiao Tong Univ Natl Engn Lab Visual Informat Proc & Applicat Xian 710049 Peoples R China Xi An Jiao Tong Univ Coll Artificial Intelligence Xian 710049 Peoples R China Northwestern Polytech Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Sch Artificial Intelligence Xian 710072 Peoples R China

One of the hottest topics in unsupervised learning is how to efficiently and effectively cluster large amounts of unlabeled data. To address this issue, we propose an orthogonal conceptual factorization (OCF) model to increase clustering effectiveness by restricting the degree of freedom of matrix factorization. In addition, for the OCF model, a fast optimization algorithm containing only a few low-dimensional matrix operations is given to improve clustering efficiency, as opposed to the traditional CF optimization algorithm, which involves dense matrix multiplications. To further improve the clustering efficiency while suppressing the influence of the noises and outliers distributed in real-world data, an efficient correntropy-based clustering algorithm (ECCA) is proposed in this article. Compared with OCF, an anchor graph is constructed and then OCF is performed on the anchor graph instead of directly performing OCF on the original data, which can not only further improve the clustering efficiency but also inherit the advantages of the high performance of spectral clustering. In particular, the introduction of the anchor graph makes ECCA less sensitive to changes in data dimensions and still maintains high efficiency at higher data dimensions. Meanwhile, for various complex noises and outliers in real-world data, correntropy is introduced into ECCA to measure the similarity between the matrix before and after decomposition, which can greatly improve the clustering effectiveness and robustness. Subsequently, a novel and efficient half-quadratic optimization algorithm was proposed to quickly optimize the ECCA model. Finally, extensive experiments on different real-world datasets and noisy datasets show that ECCA can archive promising effectiveness and robustness while achieving tens to thousands of times the efficiency compared with other state-of-the-art baselines.

关键词： Matrix decomposition clustering algorithms Robustness Optimization clustering methods Linear programming Noise measurement Anchor graph clustering correntropy orthogonal conceptual factorization (OCF)

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scBKAP: A clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 2007-2015页

作者： Wang, Xiaolin Gao, Hongli Qi, Ren Zheng, Ruiqing Gao, Xin Yu, Bin Qingdao Univ Sci & Technol Coll Math & Phys Qingdao 266061 Shandong Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China King Abdullah Univ Sci & Technol KAUST Computat Biosci Res Ctr CBRC Thuwal 23955 Saudi Arabia Qingdao Univ Sci & Technol Coll Informat Sci & Technol Sch Data Sci Qingdao 266061 Shandong Peoples R China Univ Sci & Technol China Sch Data Sci Hefei 230027 Anhui Peoples R China

Advances in single-cell RNA sequencing (scRNA-seq) technologies allow researchers to analyze the genome-wide transcription profile and to solve biological problems at the individual-cell resolution. However, existing clustering methods on scRNA-seq suffer from high dropout rate and curse of dimensionality in the data. Here, we propose a novel pipeline, scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimensionality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells. Comprehensive experiments demonstrate scBKAP's superior performance over nine state-of-the-art single-cell clustering methods on 21 public scRNA-seq datasets and simulated datasets. The source codes and datasets are available at https://***/YuBinLab-QUST/scBKAP/ and https://***/10.24433/CO.4592131.v1.

关键词： clustering algorithms Data models Pipelines Dimensionality reduction clustering methods Standards Sequential analysis Autoencoder network bisecting K-means clustering Index Terms MPDR dimensionality reduction scRNA-seq

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An improved transient search optimisation technique for effective data clustering

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International Journal of Grid and Utility Computing 2025年第3期16卷 279-295页

作者： Thakral, Prateek Kumar, Pardeep Kumar, Yugal Jaypee University of Information Technology Himachal Pradesh Solan India Chandigarh Campus Chandigarh Sarangpur India

Data clustering is a fundamental task in the field of machine learning which involves the partitioning of the datasets into meaningful groups. The traditional clustering algorithms often struggle with issues such as initial centroid sensitivity, slow convergence, and local optima trap. On the other side, meta-heuristic algorithms consist of innovative paradigms to handle these issues. Hence, this work introduces a new meta-heuristic algorithm, called transient search optimisation (TSO) to alleviate the issues of traditional clustering algorithms. Further, some enhancements are included in TSO algorithm to generate more optimal results. These improvements aim to make TSO more reliable for data clustering problems. The efficiency of the TSO is evaluated over benchmark datasets and results are compared using intra cluster distance, accuracy rate and detection rate parameters. The average accuracy rate and average detection rate of the proposed TSO algorithm are 6.94% and 6.48% higher respectively as compared to other algorithms. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： clustering algorithms

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Fusion of Centroid-Based clustering With Graph clustering: An Expectation-Maximization-Based Hybrid clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 4068-4082页

作者： Uykan, Zekeriya Amer Univ Middle East Coll Engn & Technol Egaila Kuwait

This article extends the expectation-maximization (EM) formulation for the Gaussian mixture model (GMM) with a novel weighted dissimilarity loss. This extension results in the fusion of two different clustering methods, namely, centroid-based clustering and graph clustering in the same framework in order to leverage their advantages. The fusion of centroid-based clustering and graph clustering results in a simple ``soft'' asynchronous hybrid clustering method. The proposed algorithm may start as a pure centroid-based clustering algorithm (e.g., k-means), and as the time evolves, it may eventually and gradually turn into a pure graph clustering algorithm [e.g., basic greedy asynchronous distributed interference avoidance (GADIA) (Babadi and Tarokh, 2010)] as the algorithm converges and vice versa. The ``hard'' version of the proposed hybrid algorithm includes the standard Hopfield neural networks (and, thus, Bruck's Ln algorithm by (Bruck, 1990) and the Ising model in statistical mechanics), Babadi and Tarokh's basic GADIA in 2010, and the standard k-means (Steinhaus, 1956), (MacQueen, 1967) [i.e., the Lloyd algorithm (Lloyd, 1957, 1982)] as its special cases. We call the ``hard version'' of the proposed clustering as ``hybrid-nongreedy asynchronous clustering (H-NAC).'' We apply the H-NAC to various clustering problems using well-known benchmark datasets. The computer simulations confirm the superior performance of the H-NAC compared to the k-means clustering, k-GADIA, spectral clustering, and a very recent clustering algorithm structured graph learning (SGL) by Kang et al. (2021), which represents one of the state-of-the-art clustering algorithms.

关键词： clustering algorithms clustering methods Partitioning algorithms Wireless communication Standards Principal component analysis Image segmentation Bruck's Ln algorithm centroid-based clustering expectation-maximization (EM) greedy asynchronous distributed interference avoidance (GADIA) graph clustering hybrid-nongreedy asynchronous clustering (H-NAC) k-means clustering

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An Effective clustering Optimization Method for Unsupervised Linear Discriminant Analysis

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第4期35卷 3444-3457页

作者： Wang, Quan Wang, Fei Ren, Fuji Li, Zhongheng Nie, Feiping Jiaotong Univ Natl Engn Lab Visual Informat Proc & Applicat Xian 710049 Peoples R China Jiaotong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Tokushima Univ Fac Engn Tokushima 7708506 Japan Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

The recent work Unsupervised Linear Discriminant Analysis (Un-LDA) completes its clustering process during the alternating optimization by converting equivalently the objective and finally using the K-means algorithm. However, the K-means algorithm has its inherent drawbacks. It is hard for the K-means algorithm to deal well with some complex clustering cases where there are too many real clusters or non-convex clusters. In this paper, a novel clustering optimization method is presented to accomplish the clustering process in Un-LDA and the resulting method can be named Un-LDA(CD). Specifically, instead of the K-means algorithm, an elaborately designed coordinate descent algorithm is adopted to obtain the clusters after the objective function goes through a series of simple but deft equivalent conversions. Extensive experiments have demonstrated that the coordinate descent clustering solution for Un-LDA can outperform the original K-means based solution on the tested data sets especially those complex data sets with a pretty large number of real clusters.

关键词： Dimensionality reduction clustering methods clustering algorithms Linear programming Task analysis Optimization methods Linear discriminant analysis clustering optimization method unsupervised learning coordinate descent dimensionality reduction linear discriminant analysis

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