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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.

This paper mainly focuses on identifying the limitations of the K-Means algorithm and to propose the parallelization of the K-Means using Firefly based clustering method. The new parallel architecture can handle large number of clusters. Modified Firefly algorithm can be used to find initial optimal cluster centroid and then K-Means algorithm with optimized centroid can be used to refine them and improve clustering accuracy. The final convergence issue is also addressed and solved to a great extent. The design methodology is explained in the subsequent sections. Finally, modified algorithm is compared with Parallel K-Means. It is demonstrated with experiments and it has been found that the performance of modified algorithm is better than that of the existing algorithm. Four typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques

This paper mainly focuses in identifying the limitations of the k means algorithm and to propose the parallelization of the k-means using firefly based clustering method. The new parallel architecture can handle large number of clusters. Firefly algorithm to find initial optimal cluster centroid and then k-means algorithm with optimized centroid to refined them and improve clustering accuracy. The final convergence issue is also addressed and solved to a great extent. Finally modified algorithm is compared with parallel k means is demonstrated with experiments and it has been found that the performance of modified algorithm is better than the existing algorithm. Four typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques. To achieve this we can use fork/join method in java programming. It is the most effective design method for achieve good parallel performance

In various domains, big data play crucial and related processes because of the latest developments in the digital planet. Such irrepressible data growth has led to bring clustering algorithms to segment the data into small sets to perform associated processes with them. However, the challenge continues in dealing with large data, because most of the algorithms are compatible only with small data. However, 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 the picture to provide crucial contribution towards clustering large data. This insists the need of having scalable parallel clustering to solve the aforesaid problems. In this paper, we have developed a scalable parallel clustering algorithm called Possibilistic Fuzzy C-Means (PFCM) clustering to cluster large data. So, our ultimate aim is to design and develop an algorithm in parallel way by considering data. The parallel architecture includes, splitting the input data and clustering each set of data using PFCM. Then the genetic firefly algorithm applied to the merged cluster data, which will provide better clustering accuracy in merge data. The experimental analysis will be carried out to evaluate the feasibility of the scalable Possibilistic Fuzzy C-Means (PFCM) clustering approach. The experimental analysis showed that the proposed approach obtained upper head over existing method in terms of accuracy and time.