Comparison of blocking and hierarchical ways to find cluster

Abstract

Clustering in data mining is a process of discovering groups in a set of data such that the similarity within the group is maximized and the similarity among the groups is minimized. One way of approaching clustering is to treat it as a blocking problem of minimizing the maximum distance between any two units within the same group. This method is known as Threshold blocking. It works by applying blocking as a graph partition problem. Chameleon is a hierarchical clustering algorithm, that based on dynamic modelling measures the similarity between two clusters. In the clustering process, to merge two cluster, we check if the inter-connectivity and closeness between two clusters are high relative to the internal inter-connectivity of the clusters and closeness of items within the clusters. This way of merging of cluster using the dynamic model helps in discovery of natural and homogeneous clusters. The main goal of this project is to implement a local implementation of CHAMELEON and compare the output generated from Chameleon against Threshold blocking algorithm suggested by Higgins et al with its hybridized form and unhybridized form.