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The challenging issue in microarray technique is to analyze and interpret the large volume of data. This can be achieved by clustering techniques in data mining. In hard clustering like hierarchical and k-means clustering techniques, data is divided into distinct clusters, where each data element belongs to exactly one cluster so that the outcome of the clustering may not be correct in many times. The problems addressed in hard clustering could be solved in fuzzy clustering technique. Among fuzzy based clustering, fuzzy c means (FCM) is the most suitable for microarray gene expression data. The problem associated with fuzzy c-means is the number of clusters to be generated for the given dataset needs to be specified in prior. The main objective of this proposed Possibilistic fuzzy c-means method is to determine the precise number of clusters and interpret the same efficiently. The PFCM is a good clustering algorithm to perform classification tests because it possesses capabilities to give more importance to topicalities or membership values. PFCM is a hybridization of PCM and FCM that often avoids various problems of PCM, FCM and FPCM. Based on the sample dataset „lung‟ the entire research has been developed. The available research works already developed in this area are not exclusively working with cancer genes.At this juncture, using of the Modified Possibilitistic fuzzy cmeans algorithm could be found matching with cancer genes in a better fashion. “Matlab” is used for the algorithm.The accuracy of the dataset may be identified with the usage of different training sets.Possibilistic fuzzy c means algorithm has provided better results while identifying the cancer gene. For evaluating the feasibility of the Possibilistic Fuzzy C-Means (PFCM) clustering approach, the researcher has carried out the experimental analysis.

Join and Fork model has been shown to be a powerful approach for boosting a system performance. The programming language Java supports the multithreading programming as part of the language itself instead of treating threads through the operating system. We tested several clustering algorithms implemented with Java language using multithreading approach and Join Fork approach on multi-core CPU. We try to exploit computational power from the multicore processors. Performance is increased on single core and multiple cores CPU in different ways in complexity of the algorithm and the platform. 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. This paper analyzes about the performance of Modified Parallel K Means algorithm and Parallel Genetic K Means algorithm using Java Join and Fork Method. Fork/join method overcomes deficiencies of multithreaded execution.

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.