e-ISSN : 0975-3397
Print ISSN : 2229-5631
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ABSTRACT

Title : An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining
Authors : Amreen Khan, Prof. Dr. N.G.Bawane, Prof. Sonali Bodkhe
Keywords : Particle Swarm Optimization (PSO), Fuzzy C-Means Clustering (FCM), Data Mining, Data Clustering .
Issue Date : July 2010
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 partitioning 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 algorithms, 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.
Page(s) : 1363-1366
ISSN : 0975–3397
Source : Vol. 2, Issue.4

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