Rough Fuzzy Clustering Using Decision Theory: a Data Mining Approach

Clustering is the unsupervised classification of patterns into groups. In non-fuzzy or hard clustering, data is divided into crisp clusters, where each data point belongs to exactly one cluster. In fuzzy clustering, the data points can belong to more than one cluster, and associated with each of the points are membership grades which indicate the degree to which the data points belong to the different clusters. Different clustering algorithms produce clusters with different characteristics. In this Book specifies the Clustering techniques using decision theory.