Asian Journal of Engineering and Technology Innovation

Application of Parallel Glowworm Swarm Optimization Algorithm for Data Clustering on Large Datasets

Hajira Tabasum M* Akram Pasha

Reva Institute of Technology and Management, Rukmini Knowledge Park, Kattigenahalli, Yelahanka, Near Border Security Bustop, Bengaluru, Karnataka-560064, India.

Abstract

Data Analyzing is the primary task with unstructured large data sets which is the major concern in many application areas. Many data analyzing algorithms need to be modified in order to handle the large data sizes efficiently. In this work, Glowworm Swarm Optimization for data clustering algorithm is designed and implemented to handle unstructured large data sets. The algorithm uses the optimized glowworm swarm to evaluate the data analyzing problem. The algorithm for data analyzing is used as it is very advantageous in resolving problems with multimodality, which in terms of clustering means finding the number of centroids. Glowworm Swarm Optimization for data clustering algorithm uses the Map Reduce methodology for the parallelization since it provides balancing the load in a parallel fashion, localizing the data and tolerant towards fault. The experiments conducted on various data sets shows that scalability with large unstructured data sets and achieves a better performance with data clustering, thus proving the Glowworm Swarm Optimization for data clustering algorithm is efficient compared to the traditional algorithms on clustering of large unstructured data sets.

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