Approach for Mining Multiple Patterns from Cluster

Abstract

Approach for multiple pattern extraction from obtained individual clusters is presented in this paper. Pattern extraction supports the end users in understanding the cluster concept. Pattern discovery approach utilizes the concept of reduct from rough set theory to find out non-significant attributes in a cluster which has no role in pattern formation. These non-significant attributes (reduct) are removed and remaining attributes are ranked for their significance in the cluster. Multiple pattern formulation approach uses ranked attributes to generate concise cluster patterns. Applicability of the approach is demonstrated using soybean disease and zoo datasets from machine learning repository. Objective of applying proposed approach on soybean disease clusters and clusters of zoo animals is to obtain the patterns to describe those clusters.