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Anew graph-based clustering method with dual-feature regularization and Laplacian rank constraint

KNOWLEDGE-based SYSTEMS 2025年 309卷

作者： Zhu, Hengdong Shen, Yingshan Zhan, Choujun Wang, Fu Lee Weng, Heng Hao, Tianyong South China Normal Univ Sch Comp Sci Guangzhou Peoples R China South China Normal Univ Aberdeen Inst Data Sci & Artificial Intelligence Guangzhou Peoples R China Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong Peoples R China Guangzhou Univ Chinese Med State Key Lab Dampness Syndrome Chinese Med Affiliated Hosp 2 Guangzhou Peoples R China

The performance of graph-based clustering is commonly limited by two-stage processing (Constructing and dividing similarity graph) and the quality of similar graphs. To this end, we propose a new graph-based clustering method with dual-feature regularization and Laplacian rank constraint. Specifically, our method reveals the clustering structure and unifies the two-stage process. It imposes a Laplacian rank constraint on the similarity graph to ensure that it has C connected components. In addition, a method based on dual-feature regularization is designed to capture local data feature information from both feature extraction and adaptive regression, and is applied to an accurate distance metric learning. A reweighting optimization is integrated to learn a high-quality robust similarity graph. Comprehensive experiments on Ecoli, Yale and Yeast datasets show that our method outperforms the existing graph-based clustering methods with an average improvement of about 4%, 5% and 7% on the evaluation metrics ACC, NMI and RI, respectively.

关键词： graph-based clustering Laplacian rank constraint Feature regularization Reweighting

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A graph-based clustering method with special focus on hyperspectral imaging

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ANALYTICA CHIMICA ACTA 2020年 1097卷 37-48页

作者： Hufnagl, Benedikt Lohninger, Hans Vienna Univ Technol Inst Chem Technol & Analyt Getreidemarkt 9 A-1060 Vienna Austria

A common trait of the more established clustering algorithms such as K-Means and HCA is their tendency to focus mainly on the bulk features of the data which causes minor features to be attributed to larger clusters. For hyperspectral imaging this has the consequence that substances which are covered by only a few pixels tend to be overlooked and thus cannot be separated. If small lateral features such as particles are the research objective this might be the reason why cluster analysis fails. Therefore we propose a novel graph-based clustering algorithm dubbed GBCC which is sensitive to small variations in data density and scales its clusters according to the underlying structures. The analysis of the proposed method covers a comparison to K-Means, DBSCAN and KNSC using a 2D artificial dataset. Further the method is evaluated on a multisensor image of atmospheric particulate matter composed of Raman and EDX data as well as an FTIR image of microplastics. (C) 2019 Published by Elsevier B.V.

关键词： graph-based clustering Exploratory analysis Hyperspectral imaging Density estimation Nearest neighbors Digraph

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Scalable graph-based clustering With Nonnegative Relaxation for Large Hyperspectral Image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第10期57卷 7352-7364页

作者： Wang, Rong Nie, Feiping Wang, Zhen He, Fang Li, Xuelong Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Mech Engn Xian 710072 Shaanxi Peoples R China Xian Res Inst Hitech Xian 710025 Shaanxi Peoples R China

Hyperspectral image (HSI) clustering is very important in remote sensing applications. However, most graph-based clustering models are not suitable for dealing with large HSI due to their computational bottlenecks: the construction of the similarity matrix W, the eigenvalue decomposition of the graph Laplacian matrix L, and k-means or other discretization procedures. To solve this problem, we propose a novel approach, scalable graph-based clustering with nonnegative relaxation (SGCNR), to cluster the large HSI. The proposed SGCNR algorithm first constructs an anchor graph and then adds the nonnegative relaxation term. With this, the computational complexity can be reduced to O(nd log m + nK(2) + nKc + K-3), compared with traditional graph-based clustering algorithms that need at least O(n(2) d + n(2)K) or O(n(2) d + n(3)), where n, d, m, K, and c are, respectively, the number of samples, features, anchors, classes, and nearest neighbors. In addition, the SGCNR algorithm can directly obtain the clustering indicators, without resort to k-means or other discretization procedures as traditional graph-based clustering algorithms have to do. Experimental results on several HSI data sets have demonstrated the efficiency and effectiveness of the proposed SGCNR algorithm.

关键词： Anchor graph graph-based clustering hyperspectral image (HSI) nonnegative relaxation

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Cancer DNA Microarray Analysis Considering Multi-subclass with graph-based clustering Method

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JOURNAL OF BIOSCIENCE AND BIOENGINEERING 2008年第5期106卷 442-448页

作者： Kawamura, Takashi Mutoh, Hironori Tomita, Yasuyuki Kato, Ryuji Honda, Hiroyuki Nagoya Univ Sch English Dept Biotechnol Chikusa Ku Nagoya Aichi 4648603 Japan Nagoya Univ MEXT Innovat Res Ctr Prevent Med Engn Chikusa Ku Nagoya Aichi 4648603 Japan

It is well known that various genes related to cell cycle, cell-cell adhesion, and transcriptional regulation cause the onset of cancer. Moreover, environmental factors including age, sex, and lifestyle can also contribute to the onset of cancer. Therefore, it is difficult to ascertain which factors influence the onset. Thus, patients suffering from same disease can be divided into several distinct groups. In the present study, we applied graph-based clustering to several DNA microarray datasets before the classification analysis. Several clusters formed by the graph-based clustering were used for the construction of multi-class classification model with the k-nearest neighbor and for finding genes, which are specific to a certain cluster, by One vs. Others classification. Using this approach, the classification model was constructed for four microarray datasets, leukemia, breast cancer, prostate cancer, and colon cancer, and the accuracies of classification with k-nearest neighbor were all more than 80%. And in the breast cancer dataset, we succeeded in finding genes that are specific in a cluster consisting of 38 control group samples. These results indicate the importance of sample clustering before classification model construction.

关键词： bioinformatics DNA microarray graph theory graph-based clustering cancer diagnosis

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A novel graph-based clustering method using noise cutting

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INFORMATION SYSTEMS 2020年 91卷 101504-101504页

作者： Li, Lin-Tao Xiong, Zhong-Yang Dai, Qi-Zhu Zha, Yong-Fang Zhang, Yu-Fang Dan, Jing-Pei Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China

Recently, many methods have appeared in the field of cluster analysis. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns. Algorithms based on graphs provide good results for this problem. However, some widely used graph-based clustering methods, such as spectral clustering algorithms, are sensitive to noise and outliers. In this paper, a cut-point clustering algorithm (CutPC) based on a natural neighbor graph is proposed. The CutPC method performs noise cutting when a cut-point value is above the critical value. Normally, the method can automatically identify clusters with arbitrary shapes and detect outliers without any prior knowledge or preparatory parameter settings. The user can also adjust a coefficient to adapt clustering solutions for particular problems better. Experimental results on various synthetic and realworld datasets demonstrate the obvious superiority of CutPC compared with k-means, DBSCAN, DPC, SC, and DCore. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： graph-based clustering Natural neighbors Noise cutting

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Image segmentation through graph-based clustering from surface normals estimated by photometric stereo

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ELECTRONICS LETTERS 2010年第2期46卷 134-U51页

作者： Julia, C. Moreno, R. Puig, D. Garcia, M. A. Univ Rovira & Virgili Intelligent Robot & Comp Vis Grp Dept Math & Comp Sci Tarragona Spain Univ Autonoma Madrid Dept Informat Engn Madrid Spain

A method for segmenting 2D images based on 3D shape information is proposed. First, a robust photometric stereo technique estimates the 3D normals of the objects present in the scene for every image pixel. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering. Differently from other image segmentation algorithms based on intensity, colour or texture, the regions of which are determined by the visual appearance of the depicted objects, the regions obtained with the proposed technique only depend on the 3D shapes of those objects. This can be advantageous for higher level scene understanding algorithms. This technique is especially suited to poorly illuminated scenarios and utilises a conventional camera and six inexpensive strobe lights.

关键词： 3D shape information graph theory graph-based clustering image colour analysis image pixel image resolution image segmentation image segmentation algorithms image sensors pattern clustering robust photometric stereo technique

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Multiview Robust graph-based clustering for Cancer Subtype Identification

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第1期20卷 544-556页

作者： Shi, Xiaofeng Liang, Cheng Wang, Hong Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China

Cancer subtype identification is to classify cancer into groups according to their molecular characteristics and clinical manifestations and is the basis for more personalized diagnosis and therapy. Public datasets such as The Cancer Genome Atlas (TCGA) have collected a massive number of multi-omics data. The accumulation of these datasets provides unprecedented opportunities to study the mechanism of cancers and further identify cancer subtypes at a comprehensive level. In this paper, we propose a multi-view robust graph-based clustering (MRGC) method to effectively identify cancer subtypes. Our method first learns robust latent representations from the raw omics data to alleviate the influences of the noise, where a set of similarity matrices are then adaptively learned based on these new representations. Finally, a global similarity graph is obtained by exploiting the consensus structure from the graphs. As a result, the three parts in our method can reinforce each other in a mutual iterative manner. We conduct extensive experiments on both generic machine learning datasets and cancer datasets. The experimental results confirm that our model can achieve satisfactory clustering performance compared to several state-of-the-art approaches. Moreover, we convey the practicability of MRGC by carrying out a case study on hepatocellular carcinoma.

关键词： Cancer subtyping identification robust representation learning graph-based clustering multi-view learning multi-omics data

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Outer-Points shaver: Robust graph-based clustering via node cutting

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PATTERN RECOGNITION 2020年 97卷 107001-107001页

作者： Kim, Younghoon Do, Hyungrok Kim, Seoung Bum Korea Univ Sch Ind Management Engn 145 Anam Ro Seoul 02841 South Korea

graph-based clustering is an efficient method for identifying clusters in local and nonlinear data patterns. Among the existing methods, spectral clustering is one of the most prominent algorithms. However, this method is vulnerable to noise and outliers. This study proposes a robust graph-based clustering method that removes the data nodes of relatively low density. The proposed method calculates the pseudo-density from a similarity matrix, and reconstructs it using a sparse regularization model. In this process, noise and the outer points are determined and removed. Unlike previous edge cutting-based methods, the proposed method is robust to noise while detecting clusters because it cuts out irrelevant nodes. We use a simulation and real-world data to demonstrate the usefulness of the proposed method by comparing it to existing methods in terms of clustering accuracy and robustness to noisy data. The comparison results confirm that the proposed method outperforms the alternatives. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： graph-based clustering Unsupervised learning Spectral clustering Pseudo-density reconstruction Node cutting

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Benchmarking graph-based clustering algorithms

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IMAGE AND VISION COMPUTING 2009年第7期27卷 979-988页

作者： Foggia, P. Percannella, G. Sansone, C. Vento, M. Univ Salerno Dipartimento Ingn Informaz & Ingn Elettr I-84084 Fisciano SA Italy Univ Naples Federico II Dipartimento Informat & Sistemist I-80125 Naples Italy

Among all the different clustering approaches proposed so far, graph-based algorithms are particularly suited for dealing with data that does not come from a Gaussian or a spherical distribution. They can be used for detecting clusters of any size and shape without the need of specifying the actual number of clusters;moreover, they can be profitably used in cluster detection problems. Despite of the fact that graph-based methods are gaining more and more popularity in different scientific areas, the choice of an appropriate algorithm for a given application is still the most crucial task. In this paper, we then present a detailed performance evaluation of five different graph-based clustering approaches on a database of synthetically generated graphs. The main findings of such an analysis were that algorithms based on the Minimum Spanning Tree perform better than other approaches. Four of the algorithms selected for comparison have been chosen from the open literature. While these algorithms do not require the setting of the number of clusters, they need, however, some parameters to be provided by the user. So, as the fifth algorithm under comparison, we propose an approach that overcomes this limitation, proving to be an effective solution in real applications where a completely unsupervised method for cluster detection is desirable. This was confirmed by a further comparative analysis carried out on four datasets coming from the UCI Machine Learning Repository. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Benchmarking graph-based clustering Cluster detection

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Learning doubly stochastic and nearly idempotent affinity matrix for graph-based clustering

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期299卷 1069-1078页

作者： Ah-Pine, Julien Univ Lyon Lyon 2 5 Ave Pierre Mendes France F-69500 Bron France Univ Lyon ERIC EA 3083 5 Ave Pierre Mendes France F-69500 Bron France Univ Clermont Auvergne LMBP UMR 6620 3 Pl Vasarely F-63170 Aubiere France Univ Auvergne CNRS IRD CERDI F-63000 Clermont Ferrand France

In graph-based clustering, a relevant affinity matrix is crucial for good results. Double stochasticity of the affinity matrix has been shown to be an important condition, both in theory and in practice. In this paper, we emphasize idempotency as another key condition. In fact, a theorem from Sinkhorn, R. (1968) allows us to exhibit the bijective relationship between the set of doubly stochastic and idempotent matrices of order n (modulo permutation of rows and columns) on the one hand, and the set of possible partitions of a set of n objects on the other hand. Thereby, both properties are necessary and sufficient conditions for properly modeling the clustering or graph partitioning tasks using matrices. Yet, this leads to a NP-hard discrete optimization problem. In this context, our main contribution is the introduction of a new relaxed model that efficiently learns a double stochastic and nearly idempotent affinity matrix for graph-based clustering. Our approach leverages existing properties between doubly stochastic and idempotent matrices on the one hand, and their associated Laplacian matrices on the other hand. The resulting optimization problem is bi-convex and can be addressed by an Alternating Direction Method of Multipliers scheme. Furthermore, our model requires less parameters to set in contrast to most of recent works. The experimental results we obtained using several real-world benchmarks, exhibit the interest of our method and the importance of taking into account idempotency in graph-based clustering. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Machine learning graph-based clustering Doubly stochastic affinity matrix Idempotent matrix ADMM

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