Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subsp...Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.展开更多
In this paper, we introduce the definition of L-fuzzy vector subspace, define its dimension by an L-fuzzy natural number. For a finite-dimensional L-fuzzy vector subspace, we prove that the equality holds without any ...In this paper, we introduce the definition of L-fuzzy vector subspace, define its dimension by an L-fuzzy natural number. For a finite-dimensional L-fuzzy vector subspace, we prove that the equality holds without any restricted conditions. At the same time, we deduce that the formula holds.展开更多
RNA splicing normally generates stable splice- junction sequences in viruses that are important in the context of virus mimicry. Potential variability in envelop proteins may occur with point-mutations inducing crypti...RNA splicing normally generates stable splice- junction sequences in viruses that are important in the context of virus mimicry. Potential variability in envelop proteins may occur with point-mutations inducing cryptic splice-junctions, which would remain unrecognized by T-memory cells of higher organisms in vaccine trials. Such aberrant splice- junctions result from evolution-specific non-conser- vation of actual splice-junction sites due to mutations;as such, locations of splice-junctions in a test DNA sequence could only be imprecisely specified. Such impreciseness of splice-junction locations (or cryptic sites) in a sequence is evaluated in this study via “noisy” attributes (with associated stochastics) to the mutated subspace;and, relevant fuzzy considerations are invoked with membership attributes expressed in terms of a spatial signal-to-noise ratio (SSNR). That is, SSNR adopted as a membership function expresses the belongingness of a site-region to exon/intron subspaces. An illustrative example with actual (Dengue 1 viral) DNA data is furnished demonstrating the pursuit developed in predicting aberrant splice-junctions at cryptic sites in the test sequence.展开更多
In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measurin...In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual distribution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.展开更多
基金supported in part by the National Natural Science Foundation of China (Nos. 61303074, 61309013)the Programs for Science, National Key Basic Research and Development Program ("973") of China (No. 2012CB315900)Technology Development of Henan province (Nos.12210231003, 13210231002)
文摘Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.
文摘In this paper, we introduce the definition of L-fuzzy vector subspace, define its dimension by an L-fuzzy natural number. For a finite-dimensional L-fuzzy vector subspace, we prove that the equality holds without any restricted conditions. At the same time, we deduce that the formula holds.
文摘RNA splicing normally generates stable splice- junction sequences in viruses that are important in the context of virus mimicry. Potential variability in envelop proteins may occur with point-mutations inducing cryptic splice-junctions, which would remain unrecognized by T-memory cells of higher organisms in vaccine trials. Such aberrant splice- junctions result from evolution-specific non-conser- vation of actual splice-junction sites due to mutations;as such, locations of splice-junctions in a test DNA sequence could only be imprecisely specified. Such impreciseness of splice-junction locations (or cryptic sites) in a sequence is evaluated in this study via “noisy” attributes (with associated stochastics) to the mutated subspace;and, relevant fuzzy considerations are invoked with membership attributes expressed in terms of a spatial signal-to-noise ratio (SSNR). That is, SSNR adopted as a membership function expresses the belongingness of a site-region to exon/intron subspaces. An illustrative example with actual (Dengue 1 viral) DNA data is furnished demonstrating the pursuit developed in predicting aberrant splice-junctions at cryptic sites in the test sequence.
文摘In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual distribution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.
