Big Data refers to a huge collection of data like the Banking data, social media data, repository data etc. These types of fields are responsible for day to day relevant data retrieval and processing. Clustering is one of major tasks which are done for data in order to minimize the time delay and efficient information retrieval. In this work we worked on similarity index in the form of cosine and soft cosine to count the total connection with respect to documents in the form of data. Then we use Cosine and Soft Cosine measures as hybrid Similarity algorithm to intakes the threshold policy of K means and co relation linkage property of Linkage clustering and forms new clusters. The cross-validation of the proposed work model has been done using Support Vector Machine followed by K-Mediod to improve the accuracy of clustering. This research work also focuses on different techniques of Clustering as well as classification. This research work mainly focuses on optimizing the clustering performance of the Big Data so that wealthy information can be retrieved with least cost.

Data mining, Big data, Clustering, Classification, Support Vector Machine

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