Clustering is an unsupervised learning method that constitutes a cornerstone
of an intelligent data analysis process. It is used for the exploration of
inter-relationships among a collection of patterns, by organizing them into
homogeneous clusters. Clustering has been dynamically applied to a variety of
tasks in the field of Information Retrieval (IR). Clustering has become one of
the most active area of research and the development. Clustering attempts to
discover the set of consequential groups where those within each group are more
closely related to one another than the others assigned to different groups.
The resultant clusters can provide a structure for organizing large bodies of
text for efficient browsing and searching. There exists a wide variety of
clustering algorithms that has been intensively studied in the clustering
problem. Among the algorithms that remain the most common and effectual, the
iterative optimization clustering algorithms have been demonstrated reasonable
performance for clustering, e.g. the Expectation Maximization (EM) algorithm
and its variants, and the well known k-means algorithm. This paper presents an
analysis on how partition method clustering techniques - EM, K -means and K*
Means algorithm work on heartspect dataset with below mentioned features -
Purity, Entropy, CPU time, Cluster wise analysis, Mean value analysis and inter
cluster distance. Thus the paper finally provides the experimental results of
datasets for five clusters to strengthen the results that the quality of the
behavior in clusters in EM algorithm is far better than k-means algorithm and
k*means algorithm.