A SUPERLATIVE APPROACH FOR CLUSTERING TECHNIQUES-BRIEF STUDY

Abstract

Clustering is the one of the foremost technique in the data mining and its applied in various areas such as artificial intelligence, bio-informatics, biology, computer vision, city planning, data mining, data compression, earth quake studies, image analysis, image segmentation, information retrieval, machine learning, marketing, medicine, object recognition, pattern recognition, spatial database analysis, statistics and web mining. Clustering means the act of partitioning an unlabelled dataset into groups of similar objects. The goal of clustering is to group sets of objects into classes such that similar objects are placed in the same cluster while dissimilar objects are in separate clusters. Over the past few years, several different types of biologically inspired algorithms have been proposed in the various domains. The ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Ant-based clustering is a biologically inspired data clustering technique. These algorithms have recently been shown to produce good results in a wide variety of real-world applications. During the last five years, research on and with the ant-based clustering algorithms has reached a very promising state. In this paper, a brief study on ant-based clustering algorithms is described. We also present some applications of ant-based clustering algorithms.