BibTeX
@MISC{Lecturer_dr.k.krishnamoorthy,
author = {Senior Lecturer},
title = {Dr. K.Krishnamoorthy},
year = {}
}
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Abstract
Ant-based techniques, in the computer sciences, are designed those who take biological inspirations on the behavior of these social insects. Data clustering techniques are classification algorithms that have a wide range of applications, from Biology to Image processing and Data presentation. Since real life ants do perform clustering and sorting of objects among their many activities, we expect that an study of ant colonies can provide new insights for clustering techniques. The aim of clustering is to separate a set of data points into self-similar groups such that the points that belong to the same group are more similar than the points belonging to different groups. Each group is called a cluster. Data may be clustered using an iterative version of the Fuzzy C means (FCM) algorithm, but the draw back of FCM algorithm is that it is very sensitive to cluster center initialization because the search is based on the hill climbing heuristic. The ant based algorithm provides a relevant partition of data without any knowledge of the initial cluster centers. In the past researchers have used ant based algorithms which are based on stochastic principles coupled with the k-means algorithm. The proposed system in this work use the Fuzzy C means algorithm as the deterministic algorithm for ant optimization. The proposed model is used after reformulation and the partitions obtained from the ant based algorithm were better optimized than those from randomly initialized hard C Means. The proposed technique executes the ant fuzzy in parallel for multiple clusters. This would enhance the speed and accuracy of cluster formation for the required system problem. 1.
Keyphrases
k.k rishnamoorthy iterative version ant colony deterministic algorithm wide range proposed system self-similar group center initialization data presentation social insect fcm algorithm computer science required system problem real life ant data clustering technique new insight ant fuzzy stochastic principle multiple cluster ant-based technique hard mean k-means algorithm many activity initial cluster center biological inspiration ant optimization data point different group past researcher cluster formation relevant partition
