K-means Clustering - A Survey on Various Clustering Techniques

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Clustering refers to the division of data into groups of similar objects. Each group, or cluster, consists of objects that are similar to one another and dissimilar to objects in other groups. When representing a quantity of data with a relatively small number of clusters, we achieve some simplification, at the price of some loss of detail as in lossy data compression. Clustering is a form of data modeling, which puts it in a historical perspective rooted in mathematics and statistics. Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This survey focuses on clustering in data mining. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms. A variety of algorithms have recently emerged that meet these requirements and were successfully applied to real-life data mining problems.

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Keywords

Algorithms, Design, Clustering, k-means, Cluster, Location Based Services.