A Novel Hybrid Clustering Techniques Based on K-Means, PSO and Dynamic Optimization

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Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
Clustering is a process for partitioning datasets. This paper is a challenging field of research in which their potential applications pose their own special requirements. K-means is the most extensively used algorithm to find a partition that minimizes Mean Square Error (MSE) is an exigent task. The object function of the K-means is not convex and hence it may contain local minima. ACO methods are useful in problems that need to find paths to goals. Particle Swarm Optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space.
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