AN EMPIRICAL CLUSTERING TECHNIQUE

Abstract

Clustering is the unsupervised classification of patterns into groups (clusters). Clustering technique aims at identifying groups of similar objects and, therefore helps to discover dis­tribution of patterns and interesting relations in large data sets. It has been subject of wide research since it arises in many application domains in engineering, business and social sciences. Especially, in the last few years the availability of huge transactional and experimental data sets and the arising requirements for data mining created needs for clustering algorithms that scale and can be applied in diverse domains. This thesis introduces the fundamental concepts of clustering while it surveys the widely known clustering algo­rithms. In this thesis we have implemented different approaches to take advantage of clustering technique and performed an empirical study evaluating clustering results using gene-expression datasets. We have tested several different clustering algorithms and similarity...