A New Technique to Increase the Working Performance of the Ant Colony Optimization Algorithm
Reena Jindal1, Samidha D.Sharma2, Manoj Sharma3

1Reena Jindal, M.Tech Scholar, Department of Information Technology, NIIST, Bhopal (M.P), India.
2Dr. Samidha D. Sharma, HOD, Department of Information Technology, NIIST, Bhopal (M.P), India.
3Prof. Manoj Sharma, Professor, Department of Computer Science and Engineering, NIIST, Bhopal (M.P), India.
Manuscript received on 10 July 2013 | Revised Manuscript received on 18 July 2013 | Manuscript Published on 30 July 2013 | PP: 128-131 | Volume-3 Issue-2, July 2013 | Retrieval Number: B1003073213/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The DBSCALE [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. Such meta-heuristic algorithms include Ant Colony Optimization Algorithms, Particle Swarm Optimizations and Genetic Algorithm has received increasing attention in recent years. Ant Colony Optimization (ACO) is a technique that was introduced in the early 1990’s and it is inspired by the foraging behavior of ant colonies. .This paper presents an application aiming to cluster a dataset with ACO-based optimization algorithm and to increase the working performance of colony optimization algorithm used for solving data-clustering problem, proposed two new techniques and shows the increase on the performance with the addition of these techniques [5]. We bring out a new clustering initialization algorithm which is scale-invariant to the scale factor. Instead of using the scale factor while the cluster initialization, in this research we determine the number and position of clusters according to the changes of cluster density with the division an agglomeration processes. Experimental results indicate that the proposed DBSCALE has a lower execution time cost than DBSCAN, and IDBSCAN clustering algorithms. IDBSCALE-ACO has a maximum deviation in clustering correctness rate of 95.0% and an error rate of deviation in noise data clustering of 2.62%.This algorithm is proposed to solve combinatorial optimization problem by using Ant Colony algorithm.
Keywords: DBSCALE, Ant Colony Optimization Algorithm, Clustering, Large Datasets.

Scope of the Article: Algorithm Engineering