  In machine learning and data mining, Cluster analysis is one of the most
widely used unsupervised learning technique. Philosophy of this algorithm is to
find similar data items and group them together based on any distance function
in multidimensional space. These methods are suitable for finding groups of
data that behave in a coherent fashion. The perspective may vary for clustering
i.e. the way we want to find similarity, some methods are based on distance
such as K-Means technique and some are probability based, like GMM.
Understanding prominent segment of data is always challenging as multidimension
space does not allow us to have a look and feel of the distance or any visual
context on the health of the clustering.
  While explaining data using clusters, the major problem is to tell how many
cluster are good enough to explain the data. Generally basic descriptive
statistics are used to estimate cluster behaviour like scree plot, dendrogram
etc. We propose a novel method to understand the cluster behaviour which can be
used not only to find right number of clusters but can also be used to access
the difference of health between different clustering methods on same data. Our
technique would also help to also eliminate the noisy variables and optimize
the clustering result.
  keywords - Clustering, Metric, K-means, hierarchical clustering, silhoutte,
clustering index, measures
