A Review on Data Mining & Big Data, Machine Learning Techniques
J. Nageswara Rao1, M. Ramesh2

1Mr. J. Nageswara Rao, Research Scholar, Acharya Nagarjuna University, (Andhra Pradesh), India.
2Dr. M. Ramesh, Associate Professor, Department of IT, RVR & JC COE, Guntur (Andhra Pradesh), India.
Manuscript received on 30 March 2019 | Revised Manuscript received on 09 April 2019 | Manuscript Published on 27 April 2019 | PP: 914-916 | Volume-7 Issue-6S2 April 2019 | Retrieval Number: F11120476S219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Now a day’s large amount data stored from different data sources, which is increased based on KDD from various data sources . in the direction of acquire necessary along with useful facts from data sources, some of the techniques and tools to combine the vast amount of data sets. the main aspire of data mining is to mine necessary information from huge amount of data and to retrieval information. in data mining classification and clustering main techniques to organize and come together clear-cut data in a big set of data into essential collection sets of group labels. we present a complete analysis of various clustering and categorization approaches in data mining for capable of information recovery, which includes NN, BN and decision trees.
Keywords: Clustering, Supervised, Semi Supervised, KDD, Categorical Data.
Scope of the Article: Data Mining