Improved Ant Colony Clustering Algorithm and Its Performance Study
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-29
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values.
The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae.
A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm.
The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature.
Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods.
Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering.
American Psychological Association (APA)
Gao, Wei. 2015. Improved Ant Colony Clustering Algorithm and Its Performance Study. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099689
Modern Language Association (MLA)
Gao, Wei. Improved Ant Colony Clustering Algorithm and Its Performance Study. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1099689
American Medical Association (AMA)
Gao, Wei. Improved Ant Colony Clustering Algorithm and Its Performance Study. Computational Intelligence and Neuroscience. 2015. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099689
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099689