Automated Kernel Independent Component Analysis Based Two Variable Weighted Multi-view Clustering for Complete and Incomplete Dataset

摘要

In recent years, data are collected to a greater extent from several sources or represented by multiple views, in which different views express different point of views of the data. Even though each view might be individually exploited for discovering patterns by clustering, the clustering performance could be further perfect by exploring the valuable information among multiple views. On the other hand, several applications offer only a partial mapping among the two levels of variables such as the view weights and the variables weights views, developing a complication for current approaches, since incomplete view of the data are not supported by these approaches. In order to overcome this complication, proposed a Kernel-based Independent Component Analysis (KICA) based on steepest descent subspace two variables weighted clustering in this study and it is named as KICASDSTWC that can execute with an incomplete mapping. Independent Component Analysis (ICA) which exploit distinguish operations depending on canonical correlations in a reproducing kernel Hilbert space. Centroid values of the subspace clustering approaches are optimized depending on steepest descent algorithm and Artificial Fish Swarm Optimization (AFSO) algorithm for the purpose of weight calculation to recognize the compactness of the view and a variable weight. This framework permits the integration of complete and incomplete views of data. Experimental observations on three real-life data sets and the outcome have revealed that the proposed KICASDSTWC considerably outperforms all the competing approaches in terms of Precision, Recall, F Measure, Average Cluster Entropy (ACE) and Accuracy for both complete and incomplete view of the data with respect to the true clusters in the data.