In today's world data analytics is gaining popularity due to user's motivation towards online data storage. This storage is not organized because of content types and data handling schemes complexity. User aims to retrieve data in lesser time with logical outcomes as desired can be achieved by applying data mining. Clustering in data mining is one of the known categorization approach used for formation of groups of similar elements having certain properties in common with other elements. This formation sometime creates noisy result in terms of formatted clusters. It depends on various factors such as distance measures, proximity values, objective functions, categorical or numerical attribute types etc. Over the last few years various schemes are suggested by different authors for improving the performance of tradition clustering algorithms. Among them, one is ensemble based clustering. Ensemble uses the mechanism for criteria selection from newly formed clusters with a defined portioning and joining methods to generate a single result instead of multiple solutions. The generation results are affected by various environmental parameters such as number of cluster, partitioning types, proximity values, objective function etc. This paper propose a novel SMCA based ensemble clustering algorithm for improvements over the existing issues defined in the paper. At the primary level of work and analytical evaluations, it shows the promising results in near future.

Data Mining Clustering Ensemble Consensus Partitioning and Joining Criteria Proximity Value Similarity Metrics and Clustering Algorithm (SMCA)