International Journal of Computer Science and Informatics


There are large datasets in high dimensional database to solve the cluster identification problem and for identifying dense clusters in a noisy data. In this paper by using densely intra connected sub graphs our analysis identified the clusters. We have used pattern recognition algorithms for representing the graphs and solve the cluster identification problem using K-Means-Mode, single linkage clustering and K-Nearest Neighbor Algorithm. The computational analysis show that when running on 160 CPU’s, one of our algorithm can solve a cluster identification problem on a dataset with 1,000,000 data points almost 1000 times faster than on single CPU, indicating that the problem for handling large data is done in an efficient manner.





To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.