Flocking based approach for data clustering

Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been recently used to address clustering task. An overview of PSO-based clust...

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Bibliographic Details
Published in:Natural computing 2010-09, Vol.9 (3), p.767-791
Main Authors: Ahmadi, Abbas, Karray, Fakhri, Kamel, Mohamed S.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Clustering
Complex Systems
Computer Science
Data collection
Evolutionary Biology
Experiments
Foods
Fuzzy
Fuzzy logic
Fuzzy set theory
Optimization
Processor Architectures
Tasks
Theory of Computation
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Summary:Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been recently used to address clustering task. An overview of PSO-based clustering approaches is presented in this paper. These approaches mimic the behavior of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and in regions far enough from others. PSO-based clustering approaches are evaluated using different data sets. Experimental results indicate that these approaches outperform K -means, K -harmonic means, and fuzzy c -means clustering algorithms.
ISSN:1567-7818
1572-9796
DOI:10.1007/s11047-009-9173-5