Graph partitioning advance clustering technique

Details Graph partitioning advance clustering technique Graph partitioning advance clustering technique

2012-03-09

arxiv.org/abs/1203.2002v1

International Journal of Computer Science and Engineering Survey(IJCSES), February 2012, Volume 3, Number 1

Computer Science

Learning

14 pages

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Graph partitioning advance clustering technique does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Graph partitioning advance clustering technique, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Graph partitioning advance clustering technique will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-301958