<p><span>In recent times Big Data Analysis are imminent as essential area in the field of Computer Science. Taking out of significant information from Big Data by separating the data in to distinct group is crucial task and it is beyond the scope of commonly used personal machine. It is necessary to adopt the distributed environment similar to map reduce paradigm and migrate the data mining algorithm using it. In Data Mining the partition based K Means Clustering is one of the broadly used algorithms for grouping data according to the degree of similarities between data. It requires the number of K and initial centroid of cluster as input. By surveying the parameters preferred by algorithm or opted by user influence the functionality of Algorithm. It is the necessity to migrate the K means Clustering on MapReduce and predicts the value of k using machine learning approach. For selecting the initial cluster the efficient method is to be devised and united with it. This paper is comprised the survey of several methods for predicting the value of K in K means Clustering and also contains the survey of different methodologies to find out initial center of the cluster. Along with initial value of k and initial centroid selection the objective of proposed work is to compact with analysis of categorical data.</span></p>

<p><span>In recent times Big Data Analysis are imminent as essential area in the field of Computer Science. Taking out of significant information from Big Data by separating the data in to distinct group is crucial task and it is beyond the scope of commonly used personal machine. It is necessary to adopt the distributed environment similar to map reduce paradigm and migrate the data mining algorithm using it. In Data Mining the partition based K Means Clustering is one of the broadly used algorithms for grouping data according to the degree of similarities between data. It requires the number of K and initial centroid of cluster as input. By surveying the parameters preferred by algorithm or opted by user influence the functionality of Algorithm. It is the necessity to migrate the K means Clustering on MapReduce and predicts the value of k using machine learning approach. For selecting the initial cluster the efficient method is to be devised and united with it. This paper is comprised the survey of several methods for predicting the value of K in K means Clustering and also contains the survey of different methodologies to find out initial center of the cluster. Along with initial value of k and initial centroid selection the objective of proposed work is to compact with analysis of categorical data.</span></p>

In machine learning, clustering is recognized as widely used task to find hidden structure of data. While handling the massive amount of data, the traditional clustering algorithm degrades in performance due to size and mixed type of attributes. The Removal Dependency on K and Initial Centroid Selection (REDIC) algorithm is designed to handle mixed data with frequency based dissimilarity measurement for categorical attributes. The selection of initial centroids and prior decision for number of cluster improves the efficiency of REDIC algorithm. To deal with the large scale data, the REDIC algorithm is migrated to Map Reduce paradigm,and Map Reduce based REDIC(MR-REDIC) algorithm is proposed. The large amount of data is divided into small chunks and parallel approach is used to reduce the execution time of algorithm.The proposed algorithm inherits the feature of REDIC algorithm to cluster the data.The algorithm is implemented in Hadoop environment with three different configuration and evaluated using five bench mark data sets. Experimental results show that the Speed up value of data is gradually shifting towards linear by increasing number of data nodes from one to four. The algorithm also achieves the near to closer value for Scale up parameter, while maintaining the accuracy of algorithm.

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