Partially ordered rough ensemble clustering for multigranular representations

Abstract

Clustering groups objects based on their similarity using unsupervised learning. Clustering is an NP hard problem. A number of clustering algorithms use heuristics to create a reasonable grouping of objects. However, clustering schemes created by different heuristic algorithms do not always completely agree with each other. For example, an object may belong to different clusters for different algorithms. Therefore, researchers have proposed a number of clustering ensemble techniques to combine the clustering schemes from different algorithms. This paper proposes a Rough Set based ensemble method for preserving the inherent order in clustering. The proposal is demonstrated with the help of daily price patterns of commodities, which are grouped based on Black Scholes volatility index as well as the distribution of prices.