  Clustering is a common technique for statistical data analysis, Clustering is
the process of grouping the data into classes or clusters so that objects
within a cluster have high similarity in comparison to one another, but are
very dissimilar to objects in other clusters. Dissimilarities are assessed
based on the attribute values describing the objects. Often, distance measures
are used. Clustering is an unsupervised learning technique, where interesting
patterns and structures can be found directly from very large data sets with
little or none of the background knowledge. This paper also considers the
partitioning of m-dimensional lattice graphs using Fiedler's approach, which
requires the determination of the eigenvector belonging to the second smallest
Eigenvalue of the Laplacian with K-means partitioning algorithm.
