Research Article Open Access

An Efficient Way for Clustering Using Alternative Decision Tree

E. Gothai1 and P. Balasubramanie1
  • 1 ,
American Journal of Applied Sciences
Volume 9 No. 4, 2012, 531-534

DOI: https://doi.org/10.3844/ajassp.2012.531.534

Submitted On: 10 October 2011 Published On: 13 February 2012

How to Cite: Gothai, E. & Balasubramanie, P. (2012). An Efficient Way for Clustering Using Alternative Decision Tree. American Journal of Applied Sciences, 9(4), 531-534. https://doi.org/10.3844/ajassp.2012.531.534

Abstract

Problem statement: To Improve the quality of clustering; a Multi-Level Clustering (MLC) algorithm which produces a most accurate cluster with most closely related object using Alternative Decision Tree (ADT) technique is proposed. Approach: Our proposed method combines tree projection and condition for clustering formation and also is capable to produce a customizable cluster for varying kind of data along with varying number of cluster. Results: The experimental results shows that the proposed system has lower computational complexity, reduce time consumption; most optimize way for cluster formulation and clustering quality compared is compared effectively. Conclusion: The new method offers more accuracy of cluster data without manual intervention at the time of cluster formation. Compared to existing clustering algorithms either partition or hierarchical, our new method is more robust and easy to reach the solution of real world complex business problem.

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Keywords

  • Multi-level clustering
  • clustering quality
  • decision tree algorithm
  • most optimize
  • cluster data
  • clustering algorithm
  • cluster formation
  • either partition
  • without manual