Cluster analysis finds its place in many applications especially in data analysis, image processing, pattern recognition, market research by grouping customers based on purchasing pattern, classifying documents on web for information discovery, outlier detection applications and act as a tool to gain insight into the distribution of data to observe characteristics of each cluster. This ensures that cluster places its identity in all domains. This paper presents the clustering validity measures which evaluates the results of clustering algorithms on data sets with the three main approaches of cluster validation techniques namely internal, external and relative criteria. Also it validates the cluster using the cluster indices namely Dunn’s index, DaviesBoludin index and Generalized Dunn Index using K-mean and Chameleon algorithm.
[1]
S. Angel Latha Mary,et al.
A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset
,
2012
.
[2]
Donald W. Bouldin,et al.
A Cluster Separation Measure
,
1979,
IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3]
William M. Rand,et al.
Objective Criteria for the Evaluation of Clustering Methods
,
1971
.
[4]
P. Rousseeuw.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
,
1987
.
[5]
T. Caliński,et al.
A dendrite method for cluster analysis
,
1974
.
[6]
C. Mallows,et al.
A Method for Comparing Two Hierarchical Clusterings
,
1983
.
[7]
Michalis Vazirgiannis,et al.
Clustering validity checking methods: part II
,
2002,
SGMD.