Handling Big Data with Fuzzy Based Classification Approach

Abstract

Big data is a collection of very large and complex data that is difficult to load into the computer memory. The major challenges include searching, categorization and analysis of big data. In this paper, a fuzzy based supervised classifier is proposed to handle the searching, storage and categorization of big data. In this classifier, we proposed a Random Sampling Iterative Optimization Fuzzy c-Means (RSIO-FCM) clustering algorithm which partitions the big data into various subsets. These subsets adequately cover all the instances (object space) of big data. Then, clustering is performed on these subsets by feeding forward the centers of clustered subset to group remaining subsets. Further, the designed classifier based on Bayesian theory is used to assign the labels to these clusters and also used to predict labels of unknown instances. Thus, the proposed approach results in effective clusters formation which also eliminates the problem of overlapping cluster centers faced by algorithm discussed in [1] named as Simple Random Sampling plus Extension FCM (rseFCM). The effectiveness of proposed clustering algorithm over rseFCM clustering is evaluated on two very large benchmark datasets in terms of fuzzification parameter m, objective function, computational time and accuracy. Experimental results demonstrate that, the RSIO-FCM algorithm generates more appropriate cluster centers location due to which it achieves better classification accuracy as compared to the rseFCM algorithm. Thus, it observed that, cluster centers location will have significant impact over classification results.