CFP last date
20 January 2025
Reseach Article

An Enhanced Incremental Leader Ant Clustering with Constraints

by K.sumangala, D. Vasanthi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 20
Year of Publication: 2012
Authors: K.sumangala, D. Vasanthi
10.5120/5820-8134

K.sumangala, D. Vasanthi . An Enhanced Incremental Leader Ant Clustering with Constraints. International Journal of Computer Applications. 42, 20 ( March 2012), 42-48. DOI=10.5120/5820-8134

@article{ 10.5120/5820-8134,
author = { K.sumangala, D. Vasanthi },
title = { An Enhanced Incremental Leader Ant Clustering with Constraints },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 20 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number20/5820-8134/ },
doi = { 10.5120/5820-8134 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:51.472269+05:30
%A K.sumangala
%A D. Vasanthi
%T An Enhanced Incremental Leader Ant Clustering with Constraints
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 20
%P 42-48
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering task aims at the unsupervised classification of patterns in different groups. Ant-based clustering is a biologically inspired data clustering technique. In the research, three new variants of the Leader Ant Clustering with Constraint algorithm (ILAMC, ILAME and ILACE) are proposed that implements incremental Leader Ant-based clustering and the following constraints: the must-link (ML), cannot-link (CL) constraints and ? –constraints. The main aim of the research is to improve the clustering accuracy, reduce the execution time and providing better convergence, to validate the accuracy using the F-measure and Entropy.

References
  1. Andre L. Vizine,Leandro N. de Castro, Eduardo R. Hrusehka,Ricardo R. Gudwin,Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm
  2. Bob McKay, Bo Liu,Jiuhui Pan, Incremental Clustering Based on Swarm Intelligence.
  3. Bernadette-Meunier, Leader Ant Clustering With Constraints,
  4. I. Davidson, M. Ester and S. S. Ravi, "Clustering with constraints: Feasibility issues and the K-means algorithm", in proc. SIAM SDM 2005, Newport Beach, USA.
  5. I. Davidson, M. Ester and S. S. Ravi, "Agglomerative hierarchical clustering with constraints: Theoretical and empirical results", in Proc. of Principles of Knowledge Discovery from Databases, PKDD 2005.
  6. B. Hölldobler and E. Wilson (1990), The Ants, Chapter colony odor and kin recognition. p. 197-208. Spinger Verlag, Berlin, Germany.
  7. Daniel Barbará, Julia Couto, Yi Li, COOLCAT: An Entropy-based Algorithm for Categorical Clustering, Proceedings of the Eleventh International Conference on Information and KnowledgeManagement, 582-589, 2002.
  8. D. Klein, S. D. Kamvar and C. D. Manning, "From Instance-Level constraintes to space-level constraints: Making the most of Prior Knowledge in Data Clustering", in proc. 19th Intl. on Machine Learning (ICML 2002), Sydney, Australia, Jyly 2002, pp. 307-314.
  9. N. Monmarche, M. Slimane and G. Venturini (1999), "On improving clustering in numerical databases with artificial ants", in D. Florence, J. Nicoud and F. Mondala, LNAI, Swiss Federal Institute of Technology, Lausanne, Switzerland, pp. 626-635.
  10. Shi Yong; Zhang Ge; "Research on an improved algorithm for cluster analysis",International Conference on Consumer Electronics, Communications and Networks (CECNet), Pp. 598 – 601, 2011
  11. K. Wagstaff, C. Cardie, S. Rogers and S. Schroedl, "Constrained Kmeans clustering with background knowledge", in: Proc. Of 18th Int. Conf. on Machine Learning ICML'01, pp. 577 - 584.
  12. K. Wagstaff, Intelligent clustering with instance-level constraints, PhD Thesis of Computer Science, 2002, Cornell University, USA
Index Terms

Computer Science
Information Sciences

Keywords

Clustering