Weighted Delta
Factor Cluster Ensemble Algorithm for Categorical Data Clustering in Data
Mining
Sarumathi
Sengottaian1, Shanthi Natesan2, and Sharmila Mathivanan3
1Department of Information Technology, K.S.R College
of Technology, India
2Department of Computer Science and Engineering, Nandha
Engineering College, India
3Department of Information
Technology, M.Kumarasamy College of Engineering, India
Abstract: Though many cluster ensemble approaches came forward as a potential and
dominant method for enhancing the robustness, stability and the quality of
individual clustering systems, it is intensely observed that this approach in
most cases generate a final data partition with deficient information. The
primary ensemble information matrix generated in the traditional cluster
ensemble approaches results only the cluster data point relations with unknown
entries. This paper mainly denotes the improved analysis of the Link based
Cluster Ensemble (LCE) approach which overcomes the problem of degrading the
quality of clustering result and in particular it presents an efficient novel
Weighted Delta Factor Cluster Ensemble algorithm (WDFCE) which enhances the refined matrix by augmenting the
values of similitude measures between the clusters formed in the Bipartite cluster
graph. Subsequently to obtain the final ultimate cluster result, the pairwise-similarity
consensus method is used in which K-means clustering technique is applied over
the similarity measures that are formulated from the Refined Similitude Matrix
(RSM). Experimental results on few UCI datasets and synthetic dataset reveals
that this proposed method always outperforms the traditional cluster ensemble
techniques and individual clustering algorithms.
Keywords: Clustering,
cluster ensembles, consensus function, data mining, refined matrix, similitude
measures.