AIRCC's International Journal of Computer Science and Information Technology

Ramachandra Rao Kurada ¹, K. Karteeka Pavan ², A. V. Dattareya Rao ¹

Affiliations
1 Acharya Nagarjuna University, Guntur, IN
2 Department of Information Technology, RVR and JC College of Engineering, Guntur, IN

Abstract

The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach (EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a nomenclature that highlights some aspects that are very important in the context of evolutionary data clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of MOEA design and data clustering, along with conclusions and recommendations for novice and researchers by positioning most promising paths of future research.

MOEAs have substantial success across a variety of MOP applications, from pedagogical multifunction optimization to real-world engineering design. The survey paper noticeably organizes the developments witnessed in the past three decades for EAs based metaheuristics to solve multiobjective optimization problems (MOP) and to derive significant progression in ruling high quality elucidations in a single run. Data clustering is an exigent task, whose intricacy is caused by a lack of unique and precise definition of a cluster. The discrete optimization problem uses the cluster space to derive a solution for Multiobjective data clustering. Discovery of a majority or all of the clusters (of illogical shapes) present in the data is a long-standing goal of unsupervised predictive learning problems or exploratory pattern analysis.

Keywords

Data Clustering, Multi-Objective Optimization Problems, Multiobjective Evolutionary Algorithms, Meta Heuristics.

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K. Karteeka Pavan ¹, Allam Appa Rao ², A. V. Dattatreya Rao ³, G. R. Sridhar ⁴

Affiliations
1 Department of Computer Applications, Rayapati Venkata Ranga Rao and Jagarlamudi Chadramouli College of Engineering, Guntur, IN
2 Jawaharlal Nehru Technological University, Kakinada, IN
3 Department of Statistics, Acharya Nagarjuna University, Guntur, IN
4 Endocrine and Diabetes Centre, Andhra Pradesh, IN

Abstract

Selection of initial seeds greatly affects the quality of the clusters and in k-means type algorithms. Most of the seed selection methods result different results in different independent runs. We propose a single, optimal, outlier insensitive seed selection algorithm for k-means type algorithms as extension to k-means++. The experimental results on synthetic, real and on microarray data sets demonstrated that effectiveness of the new algorithm in producing the clustering results.

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