Paper Info
Title
Biogeography-Based Optimisation For Data Clustering.
Abstract
Clustering is an important data analysis and data mining tool that is used in many fields and applications, which aims to find a homogeneous sets of objects based on the degree of similarity and dissimilarity of their attributes. One of the most popular techniques in data clustering is K-means, which is a simple, fast and efficient method that has been applied successfully in many fields. However, K-means has its own drawbacks like highly dependence on the initial solution and can easily trapped into local optima. In this paper, we investigate the behaviour of the newly created meta-heuristic optimisation algorithm called Biogeography-Based Optimisation (BBO) for data clustering with different initial solution generation mechanisms (random initial solution, sequential diversification initial solution, heuristic initial solution) that is based on the idea of migration of species between different habitats. To evaluate the performance of the proposed method, six UCI Machine Learning Repository data sets were used. The performance of the BBO algorithm was compared with well-known data-clustering algorithms that available in the literature, the experimental results showed that the BBO algorithm was able to obtain comparable results.
Year
DOI
Venue
2014
10.3233/978-1-61499-434-3-951
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
Clustering Analysis,Meta-heuristic,Biogeography-Based Optimisation,K-means
k-means clustering,Biogeography,Computer science,Meta heuristic,Theoretical computer science,Cluster analysis
Conference
Volume
ISSN
Citations 
265
0922-6389
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Abdelaziz I. Hammouri1836.51
Salwani Abdullah277845.60