Abstract

Sherin M. Youssef
"Enhanced Swarm-like Agents for Dynamically Adaptive Data Clustering"
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 partitional 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. These rules help every agent to find its new position in its navigation process and the clustering results emerge from the collective and cooperative behaviour of these swarm agents. The distributed, adaptive and cooperative behaviour of these agents was so powerful to explore the solution space effectively. Through the cooperative behaviour, the generations of agents were able to build knowledge and the whole population could pass information to the next generation.