Review on Data Mining Techniques with Big Data
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Abstract
The term Big Data comprises large- volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand- driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
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References
Bakshi, K.,(2012),” Considerations for big data: Architecture and approach”
Mukherjee, A.; Datta, J.; Jorapur, R.; Singhvi, R.; Haloi, S.; Akram, W., (18-22 Dec.,2012) , “Shared disk big data analytics with Apache Hadoop”
Aditya B. Patel, Manashvi Birla, Ushma Nair ,(6-8 Dec. 2012),“Addressing Big Data Problem Using Hadoop and Map Reduce”
Wei Fan and Albert Bifet “ Mining Big Data:Current Status and Forecast to the Future”,Vol 14,Issue 2,2013
Algorithm and approaches to handle large Data-A Survey,IJCSN Vol 2,Issue 3,2013
Xindong Wu , Gong-Quing Wu and Wei Ding “ Data Mining with Big data “, IEEE Transactions on Knoweledge and Data Enginnering Vol 26 No1 Jan 2014
Xu Y etal, balancing reducer workload for skewed data using sampling based partioning 2013.
X. Niuniu and L. Yuxun, “Review of Decision Trees,” IEEE, 2010 .
Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner “Decision Trees-What Are They?”
Weiss, S.H. and Indurkhya, N. (1998), Predictive Data Mining: A Practical Guide, Morgan Kaufmann Publishers, San Francisco, CA