Deriving Cluster Knowledge Using Rough Set Theory

Clustering algorithms gives general description of the clusters listing number of clusters and member entities in those clusters. It lacks in generating cluster description in the form of pattern. Deriving pattern from clusters along with grouping of data into clusters is important from data mining perspective. In the proposed approach reduct from rough set theory is employed to generate pattern. Reduct is defined as the set of attributes which distinguishes the entities in a homogenous cluster. It is observed that most of the remaining attributes in the cluster has same value for their attribute value pair. Reduct attributes are removed to formulate pattern by concatenating most contributing attributes. Proposed approach is demonstrated using benchmarking mushroom dataset from UCI repository.