High-performance link-based cluster ensemble approach for categorical data clustering

Abstract

In recent years, the clustering ensembles emerged as a problem solver for extracting the data points into clusters in an efficient way. However, still clustering poses a serious issue due to the presence of imperfect information while partitioning the data into clusters. This creates a serious issue in creating an efficient cluster with cluster ensembles. In this paper, we propose a solution to solve the degradation in clustering during data partitioning. The initial clusters are generated using firefly algorithm. A linked cluster ensemble approach uses similarity measurement using multi-viewpoint and weighted triple quality using entropy measurements that ensembles the data points into clusters. This avoids the problem of local optimum and avoids the issues arise from high-dimensional datasets and improve the quality of clustering. Here, the data partitioning is done with bipartite spectral algorithm and similarity measurement. Finally, the artificial neural network is used to generate classified results from the optimized clustered datasets. The experimental results are carried out over UCI repository datasets and the results show that the proposed method attains an effective ensemble clustering with higher clustering accuracy than the conventional ones.