Hybrid Data Clustering Approach Using K-Means and Flower Pollination Algorithm

Hybrid Data Clustering Approach Using K-Means and Flower Pollination Algorithm

R. Jensi1 and G. Wiselin Jiji2 1Manomanium Sundaranar University, India 2Dr.Sivanthi Aditanar College of Engineering, India

ABSTRACT

Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means

KEYWORDS Cluster Analysis, K-Means, Flower Pollination algorithm, global optimum, swarm intelligence, natureinspired Original Source URL: http://airccse.org/journal/acii/papers/2215acii02.pdf http://airccse.org/journal/acii/vol2.html