Clustering analysis is the problem of partitioning a set of objects O = {o1… on} into c self-similar subsets based on available data. In general, clustering of unlabeled data poses three major problems: 1) assessing cluster tendency, i.e., how many clusters to seek? 2) Partitioning the data into c meaningful groups, and 3) validating the c clusters that are discovered. We address the first problem, i.e., determining the number of clusters c prior to clustering. Many clustering algorithms require number of clusters as an input parameter, so the quality of the clusters mainly depends on this value. Most methods are post clustering measures of cluster validity i.e., they attempt to choose the best partition from a set of alternative partitions.

Clustering Cluster Tendency Reordered Dissimilarity Image VAT C-Means Clustering