An Efficient Way for Clustering Using Alternative Decision Tree

Problem statement: To Improve the quality of clustering; a Multi-Lev el Clustering (MLC) algorithm which produces a most accurate cluster wi th most closely related object using Alternative Decision Tree (ADT) technique is proposed. Approach: Our proposed method combines tree projection and condition for clustering formation a nd also is capable to produce a customizable cluste r 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 dat a without manual intervention at the time of cluster formation. Compared to existing clu stering algorithms either partition or hierarchical , our new method is more robust and easy to reach the solution of real world complex business problem.