The Experts below are selected from a list of 196842 Experts worldwide ranked by ideXlab platform
Zedong Li - One of the best experts on this subject based on the ideXlab platform.
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a spectral Clustering Method with semantic interpretation based on axiomatic fuzzy set theory
Applied Soft Computing, 2018Co-Authors: Yuangang Wang, Xiaodong Duan, Cunrui Wang, Zedong LiAbstract:Abstract Owing to good performance in Clustering non-convex datasets, spectral Clustering has attracted much attention and become one of the most popular Clustering algorithms in the last decades. However, the existing spectral Clustering Methods are sensitive to parameter settings in building the affinity matrix, which seriously jeopardizes the algorithm's immunity to noise data. Moreover, in many application domains, including credit rating and medical diagnosis, it is very important that the learned model is capable of understandability and interpretability. To make spectral Clustering competitive in both classification rate and comprehensibility, we propose a spectral Clustering Method with semantic interpretation based on axiomatic fuzzy set (AFS) theory, which integrates the representation capability of AFS and the classification competence of spectral Clustering (N-cut). The effectiveness of the proposed approach is demonstrated by using real-word datasets, and the experimental results indicate that the performance of our Method is comparable with that of classic spectral Clustering algorithms (NJW, SM, Diffuzzy, AASC and SOM-SC) and other Clustering Methods, including K-means, fuzzy c-means, and MinMax K-means. Meanwhile, the proposed Method can be used to explore the underlying clusters and give their characteristics in the form of fuzzy descriptions.
Guoliang Chen - One of the best experts on this subject based on the ideXlab platform.
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fuzzy soft subspace Clustering Method for gene co expression network analysis
International Journal of Machine Learning and Cybernetics, 2017Co-Authors: Qiang Wang, Guoliang ChenAbstract:Gene expression Clustering Methods for building gene co-expression networks suffer greatly from the biological complexity of cells. This paper proposes a fuzzy soft subspace Clustering Method for detecting overlapped clusters of locally co-expressed genes that may participate in multiple cellular processes and take on different biological functions. Process-specific cluster subspaces and interactions among different gene clusters can be extracted by this Method, providing useful information for gene co-expression networks analysis. Experiments on the yeast cell cycle benchmark microarray data have shown that this Method is effective in extracting underlying biological relationships between genes, and enhancing gene co-expression network inference.
Caiming Zhong - One of the best experts on this subject based on the ideXlab platform.
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minimum spanning tree based split and merge a hierarchical Clustering Method
Information Sciences, 2011Co-Authors: Caiming Zhong, Duoqian Miao, Pasi FrantiAbstract:Most Clustering algorithms become ineffective when provided with unsuitable parameters or applied to datasets which are composed of clusters with diverse shapes, sizes, and densities. To alleviate these deficiencies, we propose a novel split-and-merge hierarchical Clustering Method in which a minimum spanning tree (MST) and an MST-based graph are employed to guide the splitting and merging process. In the splitting process, vertices with high degrees in the MST-based graph are selected as initial prototypes, and K-means is used to split the dataset. In the merging process, subgroup pairs are filtered and only neighboring pairs are considered for merge. The proposed Method requires no parameter except the number of clusters. Experimental results demonstrate its effectiveness both on synthetic and real datasets.
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a graph theoretical Clustering Method based on two rounds of minimum spanning trees
Pattern Recognition, 2010Co-Authors: Caiming Zhong, Duoqian Miao, Ruizhi WangAbstract:Many Clustering approaches have been proposed in the literature, but most of them are vulnerable to the different cluster sizes, shapes and densities. In this paper, we present a graph-theoretical Clustering Method which is robust to the difference. Based on the graph composed of two rounds of minimum spanning trees (MST), the proposed Method (2-MSTClus) classifies cluster problems into two groups, i.e. separated cluster problems and touching cluster problems, and identifies the two groups of cluster problems automatically. It contains two Clustering algorithms which deal with separated clusters and touching clusters in two phases, respectively. In the first phase, two round minimum spanning trees are employed to construct a graph and detect separated clusters which cover distance separated and density separated clusters. In the second phase, touching clusters, which are subgroups produced in the first phase, can be partitioned by comparing cuts, respectively, on the two round minimum spanning trees. The proposed Method is robust to the varied cluster sizes, shapes and densities, and can discover the number of clusters. Experimental results on synthetic and real datasets demonstrate the performance of the proposed Method.
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divfrp an automatic divisive hierarchical Clustering Method based on the furthest reference points
Pattern Recognition Letters, 2008Co-Authors: Caiming Zhong, Duoqian Miao, Ruizhi Wang, Xinmin ZhouAbstract:Although many Clustering Methods have been presented in the literature, most of them suffer from some drawbacks such as the requirement of user-specified parameters and being sensitive to outliers. For general divisive hierarchical Clustering Methods, an obstacle to practical use is the expensive computation. In this paper, we propose an automatic divisive hierarchical Clustering Method (DIVFRP). Its basic idea is to bipartition clusters repeatedly with a novel dissimilarity measure based on furthest reference points. A sliding average of sum-of-error is employed to estimate the cluster number preliminarily, and the optimum number of clusters is achieved after spurious clusters identified. The Method does not require any user-specified parameter, even any cluster validity index. Furthermore it is robust to outliers, and the computational cost of its partition process is lower than that of general divisive Clustering Methods. Numerical experimental results on both synthetic and real data sets show the performances of DIVFRP.
Qiang Wang - One of the best experts on this subject based on the ideXlab platform.
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fuzzy soft subspace Clustering Method for gene co expression network analysis
International Journal of Machine Learning and Cybernetics, 2017Co-Authors: Qiang Wang, Guoliang ChenAbstract:Gene expression Clustering Methods for building gene co-expression networks suffer greatly from the biological complexity of cells. This paper proposes a fuzzy soft subspace Clustering Method for detecting overlapped clusters of locally co-expressed genes that may participate in multiple cellular processes and take on different biological functions. Process-specific cluster subspaces and interactions among different gene clusters can be extracted by this Method, providing useful information for gene co-expression networks analysis. Experiments on the yeast cell cycle benchmark microarray data have shown that this Method is effective in extracting underlying biological relationships between genes, and enhancing gene co-expression network inference.
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fuzzy soft subspace Clustering Method for gene co expression network analysis
Bioinformatics and Biomedicine, 2010Co-Authors: Qiang Wang, Yunming Ye, Joshua Zhexue Huang, Shengzhong FengAbstract:Clustering techniques for building gene co-expression networks suffer greatly from biological complexities. This paper proposes a fuzzy soft subspace Clustering Method for detecting overlapped clusters of locally co-expressed genes that may participate in multiple cellular processes and take on different biological functions. Process-specific feature subspaces of clusters and interrelations among different clusters can be extracted by this Method, providing useful clues for gene co-expression network analysis. Experiments on yeast cell cycle data have shown that this Method is effective in extracting biological relationships between functional gene clusters, and enhancing gene co-expression network analysis.
Yuangang Wang - One of the best experts on this subject based on the ideXlab platform.
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a spectral Clustering Method with semantic interpretation based on axiomatic fuzzy set theory
Applied Soft Computing, 2018Co-Authors: Yuangang Wang, Xiaodong Duan, Cunrui Wang, Zedong LiAbstract:Abstract Owing to good performance in Clustering non-convex datasets, spectral Clustering has attracted much attention and become one of the most popular Clustering algorithms in the last decades. However, the existing spectral Clustering Methods are sensitive to parameter settings in building the affinity matrix, which seriously jeopardizes the algorithm's immunity to noise data. Moreover, in many application domains, including credit rating and medical diagnosis, it is very important that the learned model is capable of understandability and interpretability. To make spectral Clustering competitive in both classification rate and comprehensibility, we propose a spectral Clustering Method with semantic interpretation based on axiomatic fuzzy set (AFS) theory, which integrates the representation capability of AFS and the classification competence of spectral Clustering (N-cut). The effectiveness of the proposed approach is demonstrated by using real-word datasets, and the experimental results indicate that the performance of our Method is comparable with that of classic spectral Clustering algorithms (NJW, SM, Diffuzzy, AASC and SOM-SC) and other Clustering Methods, including K-means, fuzzy c-means, and MinMax K-means. Meanwhile, the proposed Method can be used to explore the underlying clusters and give their characteristics in the form of fuzzy descriptions.
