Data Management

Pattern Recognition Algorithms for Cluster Identification Problem

Free registration required

Executive Summary

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 the authors' analysis identified the clusters. They 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 their 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.

  • Format: PDF
  • Size: 178.73 KB