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Ant Colony Optimization and Data Mining: Techniques and Trends

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Abstract:

The Ant Colony Optimization (ACO) technique was inspired by the ants' behaviour throughout their exploration for food. The use of this technique has been very successful ...Show More

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Abstract:

The Ant Colony Optimization (ACO) technique was inspired by the ants' behaviour throughout their exploration for food. The use of this technique has been very successful for several problems. Besides, Data Mining (DM) has emerged as an important technology with numerous practical applications, due to the wide availability of a vast amount of data. The collaborative use of ACO and DM is very promising. In this paper, we review ACO, DM, Classification and Clustering (popular DM tasks) and focus on the use of ACO for Classification and Clustering. Moreover, we briefly present related applications and examples and outline possible future trends of this promising collaborative use of techniques.

Published in: 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing

Date of Conference: 04-06 November 2010

Date Added to IEEE Xplore: 13 January 2011

ISBN Information:

DOI: 10.1109/3PGCIC.2010.47

Conference Location: Fukuoka, Japan

References is not available for this document.

I. INTRODUCTION

The use of various optimization techniques has evolved over the years and a variety of methods have been proposed in order to approach the optimal solution, or a set of approximate solutions to a range of problems in specific areas. Social insects like ants, perform a series of tasks as a group rather than atomically. Such a behavior illustrates a high rate of swarm intelligence and classifies ants as collaborative agents. The Ant Colony Optimization (ACO) technique was introduced in the early 1990's [5] and was mainly inspired by the ants' behaviour throughout their exploration for food. Moreover, Data Mining (DM) has been acknowledged as a key research field for several modern applications. Large-scale organizations apply various DM techniques on their data, to extract useful information and patterns [19].

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