• Apoorva Verma
• Preeti Tiwari

Big Data in general terms means lots and lots of data coming from many sources, some of them disparate, most of them unstructured all of them contain valuable insights. Several approaches have been developed like or are in the development to harness the implied power in this data and one of them is cluster analysis. Clustering is one of the most fundamental techniques used in data mining. Clustering helps to visually analyse the data and also assist in decision make. It is widely used in variety of application like marketing, insurance, surveillance, fraud detection and scientific discovery to extract useful information. In this paper we have discussed various clustering techniques used in analysis of big data and gave a comparison among them.

Hierarchical Based Clustering, Agglomerative Algorithm, Divisive Algorithm, outliers.

"Comparison of Clustering Techniques for Big Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.604-606, April-2019, Available :http://www.jetir.org/papers/JETIR1904B89.pdf

"Comparison of Clustering Techniques for Big Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp604-606, April-2019, Available at : http://www.jetir.org/papers/JETIR1904B89.pdf