A Mutual Subspace Clustering Algorithm for High Dimensional Datasets

Generation of consistent clusters is always an interesting research issue in the field of knowledge and data engineering. In real applications, different similarity measures and different clustering techniques may be adopted in different clustering spaces. In such a case, it is very difficult or even impossible to define an appropriate similarity measure and clustering criteria in the union space. The mutual subspace clustering from multiple clustering spaces is critically different from subspace clustering in one (union) clustering space. Mutual subspace clustering finds the common clusters agreed by subspace clustering in both clustering spaces, which cannot be handled by the traditional subspace clustering analysis. The partitioning model divides points in a data set into k exclusive clusters and a signature subspaces are found for each cluster, where k is the number of clusters desired by a user. This model improves the k means with the elimination of random centroid selection, using average pairwise distance and other parameters to generate consistent clusters. The experimental results have been recorded on cancer data set to state the efficiency of mutual subspace clustering.