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 7
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 modeling 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 lustering 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
Prof. Dr. N. G. Bawane , Amreen Khan , Prof. Sonali Bodkhe
Keywords
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