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An adaptive graph learning method based on dual data representations for clustering

PATTERN RECOGNITION 2018年 77卷 126-139页

作者： Liu, Tianchi Lekamalage, Chamara Kasun Liyanaarachchi Huang, Guang-Bin Lin, Zhiping Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore

Adaptive graph learning methods for clustering, which adjust a data similarity matrix while taking into account its clustering capability, have drawn increasing attention in recent years due to their promising clustering performance. Existing adaptive graph learning methods are based on either original data or linearly projected data and thus rely on the assumption that either representation is a good indicator of the underlying data structure. However, this assumption is sometimes not met in high dimensional data. Studies have shown that high-dimensional data in many problems tend to lie on an embedded nonlinear manifold structure. Motivated by this observation, in this paper, we develop dual data representations, i.e., original data and a nonlinear embedding of the data obtained via an Extreme Learning Machine (ELM)-based neural network, and propose to use them as the more reliable basis for graph learning. The resulting algorithm based on ELM and Constrained Laplacian Rank (ELM-CLR) further improves the clustering capability and robustness, while retaining the advantages of adaptive graph learning, such as not requiring any post-processing to extract cluster indicators. The empirical study shows that the proposed algorithm outperforms the state-of-the-art graph-based clustering methods on a broad range of benchmark datasets. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： graph-based clustering Constrained Laplacian rank Extreme learning machine Embedding graph Laplacian

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Dynamic graph-based label propagation for density peaks clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 115卷 314-328页

作者： Seyedi, Seyed Amjad Lotfi, Abdulrahman Moradi, Parham Qader, Nooruldeen Nasih Univ Kurdistan Dept Comp Engn Sanandaj Iran Univ Human Dev Dept Comp Sci Sulaymanyah Iraq

clustering is a major approach in data mining and machine learning and has been successful in many real-world applications. Density peaks clustering (DPC) is a recently published method that uses an intuitive to cluster data objects efficiently and effectively. However, DPC and most of its improvements suffer from some shortcomings to be addressed. For instance, this method only considers the global structure of data which leading to missing many clusters. The cut-off distance affects the local density values and is calculated in different ways depending on the size of the datasets, which can influence the quality of clustering. Then, the original label assignment can cause a "chain reaction", whereby if a wrong label is assigned to a data point, and then there may be many more wrong labels subsequently assigned to the other points. In this paper, a density peaks clustering method called DPC-DLP is proposed. The proposed method employs the idea of k-nearest neighbors to compute the global cut-off parameter and the local density of each point. Moreover, the proposed method uses a graph-based label propagation to assign labels to remaining points and form final clusters. The proposed label propagation can effectively assign true labels to those of data instances which located in border and overlapped regions. The proposed method can be applied to some applications. To make the method practical for image clustering, the local structure is used to achieve low-dimensional space. In addition, proposed method considers label space correlation, to be effective in the gene expression problems. Several experiments are performed to evaluate the performance of the proposed method on both synthetic and real-world datasets. The results demonstrate that in most cases, the proposed method outperformed some state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved

关键词： Density peaks clustering Soft clustering Label propagation graph-based clustering

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Automatic detection and annotation of eastern Caribbean sperm whale codas

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SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Gubnitsky, Guy Mevorach, Yaly Gero, Shane Gruber, David F. Diamant, Roee Univ Haifa Dept Marine Technol IL-3498838 Haifa Israel Univ Haifa Dept Marine Biol Morris Kahn Marine Res Stn IL-3498838 Haifa Israel Carleton Univ Dept Biol Ottawa ON K1S 5B6 Canada Domin Sperm Whale Project Roseau Dominica CUNY Baruch Coll New York NY 10010 USA Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb Croatia Project CETI New York NY 10003 USA Project CETI Roseau Dominica

A key technology for sperm whale (Physeter macrocephalus) monitoring is the identification of sperm whale communication signals, known as codas. In this paper we present the first automatic coda detector and annotator. The main innovation in our detector is graph-based clustering, which utilizes the expected similarity between the clicks that make up the coda. Results show detection and accurate annotation at low signal-to-noise ratios, separation between codas and echolocation clicks, and discrimination between codas from simultaneously emitting whales. Using this automatic annotator, insights into the characterization of sperm whale communication are presented. The results include new types of coda signals, analysis of the distribution of coda types among different whales and for different years, and evidence for synchronization between communicating whales in terms of coda type and coda transmission time. These results indicate a high degree of complexity in the communication system of this cetacean species. Source code and data for our system is publicly available

关键词： Animal language Sperm whale vocalization Detection of coda Coda annotation Echolocation clicks graph-based clustering Generalized Gaussian mixture model (GGMM)

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GPGC: Genetic Programming for Automatic clustering using a Flexible Non-Hyper-Spherical graph-based Approach 17

GPGC: Genetic Programming for Automatic Clustering using a F...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Lensen, Andrew Xue, Bing Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand

ISBN: (纸本)9781450349208

Genetic programming (GP) has been shown to be very effective for performing data mining tasks. Despite this, it has seen relatively little use in clustering. In this work, we introduce a new GP approach for performing graph-based (GPGC) non-hyper-spherical clustering where the number of clusters is not required to be set in advance. The proposed GPGC approach is compared with a number of well known methods on a large number of data sets with a wide variety of shapes and sizes. Our results show that GPGC is the most generalisable of the tested methods, achieving good performance across all datasets. GPGC significantly outperforms all existing methods on the hardest ellipsoidal datasets, without needing the user to pre-define the number of clusters. To our knowledge, this is the first work which proposes using GP for graph-based clustering.

关键词： Cluster Analysis Automatic clustering graph-based clustering Feature Construction Genetic Programming Evolutionary Computation

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Geometric-inspired graph-based Incomplete Multi-view clustering

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PATTERN RECOGNITION 2024年 147卷

作者： Yang, Zequn Zhang, Han Wei, Yake Wang, Zheng Nie, Feiping Hu, Di Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & ElectroNics iOPE Xian 710072 Shaanxi Peoples R China

Multi-view clustering methods group data into different clusters by discovering the consensus in heterogeneous sources, which however becomes difficult when partial views of real-world data are missing. Consequently, reducing the impact of missing views and leveraging available views are the key concerns for the Incomplete Multi-view clustering (IMvC) problem. In this research, we take an innovative, geometry-based perspective to investigate the IMvC problem under a commonly-used weight aggregation framework. We conduct a geometric analysis to understand how missing views shift the aggregation solution from the one achieved with full views, subsequently impacting the clustering result. Drawing from our analysis, we introduce a weight reallocation approach that minimizes the shift and approximates the full-view solution by reallocating the factual weight of each available view. Furthermore, we address the IMvC problem by using our reallocation method on a graph aggregation algorithm to obtain reliable clusters. Our extensive experiments demonstrate that our proposed approach outperforms previous IMvC methods, reporting superior results on four datasets with three metrics. Especially, on the Caltech101-7 dataset with 40 percent missing data, our method achieves an accuracy of 0.686, which significantly outperforms the results of other comparison methods that are no larger than 0.662. Further, our method can be used as a flexible plugin to improve other weight aggregation algorithms. The source code of this work is publicly available at https://***/bjlfzs/Geometric-Inspired-graph-based-Incomplete-Multi-view-clustering.

关键词： Multi-view clustering Incomplete view Weight aggregation Geometric analysis graph-based clustering

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EBMGC-GNF: Efficient Balanced Multi-View graph clustering via Good Neighbor Fusion

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 7878-7892页

作者： Wu, Danyang Yang, Zhenkun Lu, Jitao Xu, Jin Xu, Xiangmin Nie, Feiping Northwest A&F Univ Shaanxi Engn Res Ctr Intelligent Percept Coll Informat Engn Xianyang 712100 Peoples R China South China Univ Technol Sch Future Technol Guangzhou 510006 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Sch Comp SciMinist Ind & Information Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China

Exploiting consistent structure from multiple graphs is vital for multi-view graph clustering. To achieve this goal, we propose an Efficient Balanced Multi-view graph clustering via Good Neighbor Fusion (EBMGC-GNF) model which comprehensively extracts credible consistent neighbor information from multiple views by designing a Cross-view Good Neighbors Voting module. Moreover, a novel balanced regularization term based on p-power function is introduced to adjust the balance property of clusters, which helps the model adapt to data with different distributions. To solve the optimization problem of EBMGC-GNF, we transform EBMGC-GNF into an efficient form with graph coarsening method and optimize it based on accelareted coordinate descent algorithm. In experiments, extensive results demonstrate that, in the majority of scenarios, our proposals outperform state-of-the-art methods in terms of both effectiveness and efficiency.

关键词： Multi-view clustering graph-based clustering balanced clustering

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Incomplete multi-view clustering via kernelized graph learning

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INFORMATION SCIENCES 2023年第1期625卷 1-19页

作者： Xia, Dongxue Yang, Yan Yang, Shuhong Li, Tianrui Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Guangxi Univ Sci & Technol Sch Comp Liuzhou 545006 Peoples R China

A fundamental assumption underpinning the recent progress in multi-view clustering is the full observation of all views, which rarely holds for real-world data as they often suffer from the absence of some instances in individual views. Such incompleteness generally disables the traditional multi-view clustering models in practical applications. This paper proposes a Kernelized graph-based Incomplete Multi-view clustering (KGIMC) algorithm to overcome this limitation. The key novelty of our model is that its subtasks, e.g. similarity learning, clustering analysis, and kernel completion, are optimized in a mutual reinforcement manner to achieve an overall optimal clustering result as follows: 1) Similarity learning is directed by clustering analysis to construct a graph with as many connected components just as the number of clusters. 2) The well-constructed similarity graph in the last iteration is employed to guide the process of kernel completion. 3) The updated kernels are in turn used to conduct subsequent similarity learning. We extend our model into multi-kernel settings to alleviate the difficulty of kernel selection. We provide an alternating iterative algorithm to solve KGIMC with convergence guaranteed and complexity analyzed. Extensive experiments are conducted on several popular datasets, and the results demonstrate that KGIMC outperforms the state-of-the-art approaches in general.(c) 2023 Elsevier Inc. All rights reserved.

关键词： Incomplete multi-view clustering Multi-kernel learning graph-based clustering United optimization framework Kernel completion

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Robust optimal graph clustering

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NEUROCOMPUTING 2020年第0期378卷 153-165页

作者： Wang, Fei Zhu, Lei Liang, Cheng Li, Jingjing Chang, Xiaojun Lu, Ke Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Monash Univ Sch Informat Technol Melbourne Vic Australia

Most graph-based clustering methods separate the graph construction and clustering into two independent processes. The manually pre-constructed graph may not be suitable for the subsequent clustering. Moreover, as real world data generally contains noises and outliers, the similarity graph directly learned from them will be unreliable and further impair the subsequent clustering performance. To tackle the problems, in this paper, we propose a novel clustering framework where a robust graph is learned with noise removal, and simultaneously, with desirable clustering structure. To this end, we first learn a discriminative representation of data samples via sparse reconstruction. Then, a robust graph is automatically constructed with adaptive neighbors to each data sample. Simultaneously, a reasonable rank constraint is imposed on the Laplacian matrix of similarity graph to pursue the ideal clustering structure, where the number of connected components in the learned graph is exactly equal to the number of clusters. We finally derive an alternate optimization algorithm guaranteed with convergence to solve the formulated unified learning framework to achieve better prediction accuracy. Experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method compared with several state-of-the-art clustering techniques. (C) 2019 Elsevier B.V. All rights reserved.

关键词： graph-based clustering Sparse reconstruction Similarity graph Adaptive neighbors

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An overview of recent multi-view clustering

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NEUROCOMPUTING 2020年 402卷 148-161页

作者： Fu, Lele Lin, Pengfei Vasilakos, Athanasios V. Wang, Shiping Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Fuzhou Univ Key Lab Network Comp & Intelligent Informat Proc Fuzhou 350116 Peoples R China

With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional data that describes objects from single perspective, multi-view data is semantically richer, more useful, however more complex. Since traditional clustering algorithms cannot handle such data, multi-view clustering has become a research hotspot. In this paper, we review some of the latest multi-view clustering algorithms, which are reasonably divided into three categories. To evaluate their performance, we perform extensive experiments on seven real-world data sets. Three mainstream metrics are used, including clustering accuracy, normalized mutual information and purity. based on the experimental results and a large number of literature reading, we also discuss existing problems in current multi-view clustering and point out possible research directions in the future. This research provides some insights for researchers in related fields and may further promote the development of multi-view clustering algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Machine learning Unsupervised learning Multi-view clustering graph-based clustering Space learning

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Robust and optimal neighborhood graph learning for multi-view clustering

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INFORMATION SCIENCES 2023年 631卷 429-448页

作者： Du, Yangfan Lu, Gui-Fu Ji, Guangyan AnHui Polytech Univ Sch Comp & Informat Wuhu 241000 Anhui Peoples R China

In recent years, researchers have proposed many graph-based multi-view clustering (GMC) al-gorithms to solve the multi-view clustering (MVC) problem. However, there are still some limi-tations in the existing GMC algorithm. In these algorithms, a graph is usually constructed to represent the relationship between the samples in a view;however, it cannot represent the relationship very well since it is often preconstructed. In addition, these algorithms ignore the robustness problem of each graph and high-level information between different graphs. Then, in the paper, we propose a novel MVC method, i.e., robust and optimal neighborhood graph learning for MVC (RONGL/MVC). Specifically, we first build an initial graph for each view. However, these initial graphs cannot represent the relationship between the samples in each view well, so we look for the optimal graph with k connected components in the neighborhood of each initial graph, where k is the number of clusters. Then, to improve the robustness of RONGL/MVC, we recon-struct the optimal graph with the self-representation matrix. Furthermore, we stack all the self -representation matrices into a tensor and impose the tensor low-rank constraint, which can maximize consistent features and explore the high-order relationship between optimal graphs. In addition, we provide an optimization strategy by utilizing the Augmented Lagrange Multiplier (ALM) method. Experimental results on several datasets indicate that RONGL/MVC outperforms state-of-the-art methods.

关键词： Multi-view clustering graph-based clustering Subspace clustering Tensor Rank constraint

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