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Review on Data Mining Techniques with Big Data

TEMPALLI NARENDRA BABU, R.MADHURI DEVI, , ,

Affiliations
M.Tech (CSE), Priyadrshini Institute of Technology & Management
AssociateProfessor (Dept.of CSE), Priyadrshini Institute of Technology & Management

:NOT ASSIGNED

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.

Citation

TEMPALLI NARENDRA BABU,R.MADHURI DEVI."Review on Data Mining Techniques with Big Data". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 12,pp.1147-1152, December- 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I1259.pdf,

Keywords : HACE,Hadoop,Big Data, heterogeneity, autonomous sources, complex and evolving associations

References

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3. Aditya B. Patel, Manashvi Birla, Ushma Nair ,(6-8 Dec. 2012),â€œAddressing Big Data Problem Using Hadoop and Map Reduceâ€
 4. Wei Fan and Albert Bifet â€œ Mining Big Data:Current Status and Forecast to the Futureâ€,Vol 14,Issue 2,2013 
5. Algorithm and approaches to handle large Data-A Survey,IJCSN Vol 2,Issue 3,2013
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7. Xu Y etal, balancing reducer workload for skewed data using sampling based partioning 2013. 
8. X. Niuniu and L. Yuxun, â€œReview of Decision Trees,â€ IEEE, 2010 . 
9. Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner â€œDecision Trees-What Are They?â€
 10. Weiss, S.H. and Indurkhya, N. (1998), Predictive Data Mining: A Practical Guide, Morgan Kaufmann Publishers, San Francisco, CA

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Citation Index

Citations Indices	All
Citations	1026
h-index	14
i10-index	20
Source: Google Scholar

Acceptance Rate (By Year)

Acceptance Rate (By Year)
Year	Rate
2021	10.8%
2020	13.6%
2019	15.9%
2018	14.5%
2017	16.6%
2016	15.8%
2015	18.2%
2014	20.6%

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DOI:10.22362/ijcert

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