An Optimized Inference of Pattern Recognition Using Fuzzy Ant Based Clustering Algorithm

K. Sathesh Kumar(1*), M. Hemalatha(2)

(1) Department of Computer Science, Karpagam University, Coimbatore, India
(2) Department of Computer Science, Karpagam University, Coimbatore, India
(*) Corresponding author

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The tremendous growth in web-based technology, application and sharing of information, and knowledge discovery which have a direct impact on economy, presents voluminous data which require Data Mining Techniques. The current study presents a novel framework of data mining which clusters the data and then follows the Fuzzy Association Rule Mining. The first stage employs the Fuzzy Ant System-Based Clustering Algorithm (FASCA) and Fuzzy Ant K -means (FAK) to cluster the database, while the Fuzzy ant colony system-based Fuzzy Association Rules Mining algorithm can be applied to discover the useful rules for each group. The evaluation revealed that the intended method was not only able to mine the rules much more rapidly, but can also identify more significant rules
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Clustering; Data Mining; Fuzzy Ant Colony System; Fuzzy Association Rule

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