An Analysis of Particle Swarm Optimization with Data Clustering- Technique for Optimization in Data Mining

Data clustering is an approach for automatically finding classes, concepts, or groups of patterns. It also aims at representing large datasets by a few number of prototypes or clusters. It brings simplicity in modelling data and plays an important 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 computational requirements on the clustering techniques. Swarm Intelligence (SI) has 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 allows the particles to be robust to trace the changing environment. Data structure identifying from the large scale data has become a very important in the data mining problems. Cluster analysis identifies groups of similar data items in large datasets which is one of its recent beneficiaries. The increasing complexity and large amounts of data in the data sets that have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. This paper also proposes two new approaches using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. Citation: Anusha Chaudhary, IMSEC. "An Analysis of Particle Swarm Optimization with Data Clustering- Technique for Optimization in Data Mining." Global Research and Development Journal For Engineering 25 2017: 141 - 144.