Enhanced swarm-like agents for dynamically adaptive data clustering

Authors:
Sherin M. Youssef;Mohamed Rizk;Mohamed El-Sherif
Affiliations:
Department of Computer Engineering, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt;Department of Computer Engineering, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt;Department of Computer Engineering, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt
Venue:
CEA'08 Proceedings of the 2nd WSEAS International Conference on Computer Engineering and Applications
Year:
2008

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Abstract

Data clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. Inspired by the self-organized behaviour of bird flocks, a new dynamic clustering approach based on Particle Swarm Optimization is proposed. In this paper, we introduced the PSDC approach, new particle swarm-like agents for multidimensional data clustering. Unlike other partition clustering algorithms, this technique does not require initial partitioned seeds and it can dynamically adapt to the changes in the global shape or size of the clusters. In this technique, the agents have lots of useful features such as sensing, thinking, making decisions and moving freely in the solution space. The moving swarm-like agents are guided to move according to a specific proposed navigation rules. Numerous experiments have been conducted using both synthetic and real datasets to evaluate the efficiency of the proposed model. Cluster validity approaches are used to quantitatively evaluate the results of the clustering algorithm. The experimental results showed that the proposed particle swarm-like clustering algorithm reaches good clustering solutions and achieves superior performance compared to others.