Scalable parallel clustering using modified Firefly algorithm

Abstract

Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are more similar to each other than objects from different clusters. We try to exploit computational power from the multicore processors. We need a new design on existing algorithms and software. Firefly algorithm is one of the metaheuristic algorithms which are used for solving optimization problems. The existing clustering algorithms either handle different data types with inefficiency in handling large data or handle large data with limitations in considering numeric attributes. Hence, parallel clustering has come into picture to provide crucial contribution towards clustering large data. In this paper, we have developed a scalable parallel clustering algorithm using FA and genetic algorithm to cluster large data. Modified FA algorithm does not handle the large data effectively. So, our ultimate aim is to design and develops an algorithm in parallel way by considering data. The experimental analysis will be carried out to evaluate the feasibility of the new combined clustering approach. The experimental analysis showed that the proposed approach obtained upper head over existing method in terms of accuracy and time. Most of the programming languages doesn't provide multiprocessing facilities and hence wastage of processing resources. In order to utilize the intrinsic capabilities of a multi-core processor the software application must be able to execute tasks in parallel using all available CPUs. To achieve this we can use fork/join method in java programming. It is the most effective design method for achieve good parallel performance.