Improved Intelligent Techniques of Ensemble Data Clustering Method Using Bees Swarm Optimization Ensemble Approach

Clustering is considered as an unsupervised partitioning method which is an intelligent selforganizing system that partitions the datasets in a comparable or a different way, where each data cluster consists of similar data points. As of late, the clustering ensemble is regarded as a solution to extract the categorical data points into relevant clusters in a more effective way. However, it encounters a serious problem related to data imperfection during data partitioning into clusters. The present examination thinks about this as the primary issue and improves it using following thought. Right now the ensemble clustering over clear cut datasets using Bees Swarm Optimization (BSO) based cluster ensemble approach. The similarity measurement is carried out using entropy weighted triple quality to finds the similarity difference between the clusters. The knowledge paradigms including cognitive science and systems is intended to improve the clustering quality over categorical datasets. The result shows that the proposed method is accurate than existing methods over categorical datasets in terms of clustering accuracy, normalized mutual information and adjusted rand. -based .This shows the effectiveness of the BSO ensemble clustering algorithm than the existing link clustering ensemble algorithm.