Paper Info
Title
A data-clustering approach based on artificial ant colonies with control of emergence combined with K-Means approach
Abstract
Data-clustering mainly aims in forming the amount of unmanaged data to manageable piles, by discovering homogeneous groups. It is a combinatorial problem, because the number of partitions that can be obtained grows exponentially with the volume of data to be classified and the number of clusters. In this paper, we deal with the problem from the perspective of distributed optimization and we present a new approach for data-clustering based on combination of K-Means approach with our approach for data-clustering based on artificial ant colonies with control of emergence recently published in [14]. Since the objective is to optimize the partitioning, a comparative study will be done between K-Means approach and our method published in [14] and the combination of both of them. A multi-agent platform was used to implement the proposed approach. The obtained results in terms of internal and external performance measures on a set of real and synthetic benchmarks show the excellence of the proposed approach.
Year
DOI
Venue
2015
10.1145/2816839.2816931
Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication
DocType
ISBN
Citations 
Conference
978-1-4503-3458-7
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Billel Kenidra101.01
Mohamed Benmohammed200.68