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Artificial Bee Colony Based Multiview Clustering ABC MVC for Graph Structure Fusion in Benchmark Datasets

Journal: International Journal of Trend in Scientific Research and Development (Vol.4, No. 2)

Publication Date:

Authors : ;

Page : 969-674

Keywords : Data Miining; Multiview Clustering; Artificial Bee Colony (ABC); Multi-view Clustering (MVC); and graph structure;

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Combining data from several information sources has become a significant research area in classification by several scientific applications. Many of the recent work make use of kernels or graphs in order to combine varied categories of features, which normally presume one weight for one category of features. These algorithms don't consider the correlation of graph structure between multiple views, and the clustering results highly based on the value of predefined affinity graphs. Artificial Bee Colony is combined to Multi view Clustering ABC MVC model in order to combine each and every one of features and learn the weight for each feature with respect to each cluster separately by new joint structured sparsity inducing norms. It also solves the issue of MVC by seamlessly combining the graph structures of varied views in order to completely make use of the geometric property of underlying data structure. ABC MVC model is based on the presumption with the purpose of intrinsic underlying graph structure would assign related connected part in each graph toward the similar group. Implementation results shows that the proposed ABC MVC model gets improved clustering accuracy than the other conventional methods such as Graph Structure Fusion GSF and Multiview Clustering with Graph Learning MVGL . The results are implemented to Caltech 101 and Columbia Object Image Library COIL 20 with respect to clustering accuracy ACC , Normalized Mutual Information NMI , and Adjusted Rand Index ARI N. Kamalraj ""Artificial Bee Colony Based Multiview Clustering (ABC-MVC) for Graph Structure Fusion in Benchmark Datasets"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: Paper Url :

Last modified: 2020-05-12 19:53:41