A Secure Technique for Unstructured Big Data Using Clustering Method

In clustering process, a set of patterns is separated into disjoint and identical significant groups. Faster data analyzing is one of the important aspects of clustering method. A number of works have been reported by various authors to optimize its multidimensionality toward distinct big data sets. However, the existing techniques are unlabeled to offer an optimal solution with regards to bunching high dimensional information set as their multifaceted nature tends to make things more hazardous while quantities of measurements are included. Especially, for uncertain and unstructured data it produces very poor results. Apart from that, the searching of data from the cloud or storage systems and data security are also important aspects. The current techniques also fail to offer proper care or solution in these matters. In this paper, the authors propose a secure clustering technique for unstructured and uncertain big data, and design an algorithm SCTA. This proposed technique does also offer high dimensionally for distinct types of big data. It includes SDES encryption technique for securing data as well as to maintain low complexity. Consequently, it includes a data searching algorithm from the cloud or storage systems to search data efficiently with low complexity.