Multi-View Fuzzy Clustering with Weighted Attributes and Views
Neelesh Gothania, Sunil Kumar · 2018
The structure of data available on Internet make sit inherently multi-view data. Cluster analysis of such data requires decisions like which view or attribute is more relevant to the application that will use the output cluster labels. Moreover., hard clustering is not appropriate given the dynamic relationship among different views of data. Hence., this paper suggests a fuzzy clustering method for multi-view data that determines comparative importance of views and attributes through a weighing scheme. The weighing scheme is included within the clustering framework as a co-learning mechanism.