An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 4
Abstract
Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in odeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate artitioning of huge datasets, which may come with a variety of attributes or features. This imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired lgorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This paper looks into the use of Particle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and also allows the particles to be robust to trace the changing environment.
Authors and Affiliations
Amreen Khan , Prof. Dr. N. G. Bawane , Prof. Sonali Bodkhe
Keywords
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