TY - JFULL AU - S. Sarumathi and N. Shanthi and P. Ranjetha PY - 2015/12/ TI - Analysis of Diverse Cluster Ensemble Techniques T2 - International Journal of Computer and Information Engineering SP - 2378 EP - 2389 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10003748 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 107, 2015 N2 - Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand. ER -