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)
Multiview Clustering, Artificial Bee Colony (ABC), Multi-view Clustering (MVC), and graph structure
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