Survey of machine learning algorithms with ensemble framework for clustering applications

Data Clustering is defined as grouping of objects with similar representation of objects between two different groups. The task of data clustering in many mining applications faces severe challenges like identification of the categorical attributes, wrong merging with other clusters, deficiency of class labels, errors due to missing or empty values etc.Hence, researches over cluster ensemble reduced the difficulties in clustering the datasets. This process improves the data quality through individual clustering of data segments. As, research in this field increased to prominent level, the algorithms, application and the results has improved to a better level. Hence, this paperpresents a summary on critical analysis on the algorithms and techniques used to improve the clustering ensemble in data mining applications. Additionally, this paper includes the characteristics of the cluster ensemble and the datasets for evaluation. The discussions are compared and shown comprehensively in a tabular form. © 2017, Institute of Advanced Scientific Research, Inc. All rights reserved.