Optimizing Fuzzy Clustering using Swarm Intelligence in Data Mining

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Poonam Chalotra
Harpreet Kaur


Data mining is a powerful new technology, which aims at the extraction of hidden predictive information from large databases. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The process of knowledge discovery from databases requires fast and automatic clustering of very large datasets. It deals with large databases that impose severe computational requirements on clustering analysis. A family of nature inspired algorithms, known as Swarm Intelligence (SI), has recently emerged as tool to meet such requirements on number of real world clustering problems. Algorithms based on Swarm Intelligence are inspired from the collective intelligence emerging from the behavior of a group of social insects like bees, termites and wasps. In this paper we have discussed the use of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for clustering in Data Mining. The performance of Fuzzy C-means is enhanced when used with PSO optimization and ACO optimization.


Keywords: Swarm Intelligence (SI), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Fuzzy C-means Clustering (FCM).


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