DATA MINING APPROACH FOR BIG DATA ANALYSIS:A THEORITICAL DISCOURSE

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Published: Aug 28, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i7.4032
Keywords:
Big data analytics, Unstructured data, volume, veracity, velocity

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mudassir makhdoomi
islamia college of science and commerce

Abstract

BIG data is one of the most emerging topic studied today. Everyone is talking about big data, and it is believed that science, business, industry, government, society, etc. will undergo a thorough change with the influence of big data. The volume of data being produced is increasing at an exponential rate due to our unprecedented capacity to generate, capture and share vast amounts of data. Existing algorithms can be used to extract information from these large volumes of data. However, these algorithms are computationally expensive. In this paper we are discussing some of the major challenges and issues posed by big data and the potential solution to those challenges.

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Article Details

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Vol. 8 No. 7 (2017): July-August 2017
Section
Articles

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Author Biography

mudassir makhdoomi, islamia college of science and commerce

department of computer applications assistant professor

References

Prem, A., & Jayanthi, P. “Big data sources and data miningâ€. International Education and Research Journal,(2016) 2(4).

Zicari R , “Big Data: Challenges and Opportunities†Akerkar R (ed) Big Data Comput. Chapman and Hall/CRC (2013), p 564.

Che, D., Safran, M., & Peng, Z..†From big data to big data mining: challenges, issues, and opportunitiesâ€. In International Conference on Database Systems for Advanced Applications (2013) (pp. 1-15). Springer Berlin Heidelberg

Fan, Jianqing, Fang Han, and Han Liu, "Challenges of big data analysis." National science review (2014) 1.2: 293-314.

Dileep Kumar G., Manoj Kumar Singh,†Effective Big Data Management and Opportunities for Implementation†IGI Global(2016) ,ISBN: 9781522501824.

Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. “Big data and its technical challengesâ€. Communications of the ACM,(2014) 57(7), 86-94.

Chen, M., Mao, S., Zhang, Y., & Leung, V. C. “Big data: related technologies, challenges and future prospects “.(2014),Heidelberg: Springer pp 2-9.

Chen, M., Mao, S., & Liu, Y. “Big data: A surveyâ€. Mobile Networks and Applications, (2014),19(2), 171-209.

Dina Fawzy1, Sherin Moussa and Nagwa Badr , “The Evolution of Data Mining Techniques to Big Data Analytics: An Extensive Study with Application to Renewable Energy Data Analyticsâ€,(2016) Asian Journal of Applied Sciences (ISSN: 2321 – 089) Volume 04 – Issue 03.

Rokach, Lior. "The Role of Fuzzy Sets in Data Mining." Soft Computing for Knowledge Discovery and Data Mining. Springer US, 2008. 187-203.

Rekha, M., and M. Swapna."Role of fuzzy logic in data Mining." International Journal of Advance Research in Computer Science and Management Studies 2 (2014): 12.

Abdul Hamid, Norhamreeza.â€The effect of adaptive parameters on the performance of back propagation." (2012), PhD diss., Universiti Tun Hussein Onn Malaysia.

Bouzouita, Ines, et al. "A Comparative Study of a New Associative Classification Approach for Mining Rare and Frequent Classification Rules." (2011),International Conference on Information Security and Assurance. Springer Berlin Heidelberg.

Hans, Nivranshu, Sana Mahajan, and S. Omkar.,"Big data clustering using genetic algorithm on hadoop mapreduce." (2015), International Journal of Scientific Technology Research 4.

Jevtic, A., Gazi, P., Andina, D. and Jamshidi, M.O.,â€Building a swarm of robotic beesâ€. (2010) In World Automation Congress (WAC),(pp. 1-6). IEEE.

Grosan, C., Abraham, A., & Nicoara, M. “Performance tuning of evolutionary algorithms using particle sub swarmsâ€. In Symbolic and Numeric Algorithms for Scientific Computing, 2005. SYNASC 2005. Seventh International Symposium on (pp. 8-pp). IEEE.

Abraham, A., Grosan, C., & Ramos, V. (Eds.). “Swarm intelligence in data miningâ€(2007),Vol. 34. Springer.

Cheng, S., Zhang, Q., & Qin, Q.â€Big data analytics with swarm intelligenceâ€. Industrial Management & Data Systems (2016), 116(4) 646-666.