• Dr.K.P.N.V.Satya Sree
• Dr.S.M Roy Choudri

For to examining big data Clustering is a basic information mining and apparatus. There are troubles for applying grouping procedures to huge information twosome to new difficulties that are raised with huge information. As Big Data is alluding to TBs and PBs of information and grouping calculations are accompanied great computational outlays, the inquiry is the means by which to adapt to this issue and how to convey bunching methods to enormous information and get the outcomes in a sensible time. K-Means which is a standout amongst the most utilized bunching strategies and K-Means in view of MapReduce is considered as a propelled answer for substantial dataset grouping. Be that as it may, the executing time is as yet an obstruction because of the expanding quantity of iterations when there is an expansion of dataset extent and number of groups. This paper exhibits another approach for diminishing the quantity of emphases of K-Means calculation which can be connected to expansive dataset grouping. And furthermore this technique introduce plan to redress the issues related with k-implies significantly with the centroid choice issue. This strategy can likewise ensure the base computational time and increment in the exactness of results.

K-means. Big Data, Data Mining, Clustering.

"An Enhanced Method of Clustering for Big Data Mining using K-Means", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.216-220, June 2019, Available :http://www.jetir.org/papers/JETIR1906H03.pdf

"An Enhanced Method of Clustering for Big Data Mining using K-Means", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp216-220, June 2019, Available at : http://www.jetir.org/papers/JETIR1906H03.pdf