Weighted delta factor cluster ensemble algorithm for categorical data clustering in data mining

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.