Different clustering algorithms for Big Data analytics: A review

The era of huge data is snowballing at frequent swiftness in size (volume) and in different formats (variety). This data which comes from various sources e.g. media, communication devices, internet, business etc. and there are many difficulties and challenges that one faces while handling it. Data mining is a process intended to reconnoiter analytical data (typically business or market associated data - also acknowledged as “Big data”). There are several data mining techniques such as outlier analysis, organization, clustering, prediction and association rule mining. In this paper we have discussed several applications and the importance of clustering. To examine the huge volume of data, clustering algorithms aid in providing a powerful meta-learning tool. Numerous clustering techniques (including traditional and the recently developed) in reference to large data sets with their pros & cons are being discussed in this paper.