Gunjan Dashora, Payal Awwal
Data clustering can be considered as an essential research topic in the field of data mining. Clustering is the field of grouping similar data samples together in some way , according to some criteria. It is the process of standardizing data into meaningful groups, called clusters. A new paradigm Swarm Intelligence is a collective behaviour of social systems like insects such as ants(ant colony optimization), Fish Schooling, Particle swarm optimization, Bat algorithm etc. Advanced study have show that partitional clustering algorithms are more convenient for clustering large datasets. The most commonly used partitional clustering algorithm is K-means due to easily implementation and most efficient in terms of execution time. The drawback of K-means is that it is sensitive to the selection of initial partition and may converge to local optima. This paper consider the de-merits of standard K-means algorithm for data clustering and a comparative analysis of PSO and Bat algorithm is shown with some of their advantages.
Particle Swarm Optimization, K-Means, Bat Algorithm, Swarm Intelligence.