Infrastructure of Data Mining Technique with Big Data Analytics
Keywords:
Big data, Business development, marketing and mining data’s.Abstract
Data mining is the concept of gathering new information from huge sets of data. It is also known as Knowledgeable Discover Database (KDD).In past few years business development in KDD are rapidly high in the market. Because its processing is more useful in all kinds of business marketing field. However before the arrival of data mining, business marketing is slightly slow in process, at that time business marketing is more dependent on Television ads, sponsors then marketing executive etc...In late 90s this problem was fulfill by the development of mining the datas concept. So in this article we are discussing about how the processing way of data mining collides with big data analytics.
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References
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[15] Thuraisingham, B., “A primer for understanding and applying data mining..,” in It Professional, 2(1), 2000, pp.28-31.
[2] Xu, L., Jiang, C., Wang, J., Yuan, J. and Ren, Y., “Information security in big data: privacy and data mining. Ieee Access,” in Ieee Access. 2014, vol, pp. 1149–1176.
[3] Cheung, D.W., Lee, S.D. and Xiao, Y., “Effect of data skewness and workload balance in parallel data mining”, IEEE IEEE Transactions on Knowledge and Data Engineering, pp. 498-514, 2002.
[4] Saygin, Y. and Ulusoy, Ö., “Exploiting data mining techniques for broadcasting data in mobile computing environments,” in IEEE Transactions on Knowledge and Data Engineering, 2002, pp.1387-1399.
[5] Feng, Z. and Zhu, Y.,, “A survey on trajectory data mining: Techniques and applications,” in IEEE Access 2016, pp. 2056–2067.
[6] Cao, L., “Social security and social welfare data mining: An overview IEEE Transactions on Systems, Man, and Cybernetics, 2012,pp.837-853
[7] Ming, J., Zhang, L., Sun, J. and Zhang, Y., “Analysis models of technical and economic data of mining enterprises based on big data analysis”, IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2018, pp 224-227.
[8] Kalegele, K., Sasai, K., Takahashi, H., Kitagata, G. and Kinoshita, T., “Four decades of data mining in network and systems management,” in . IEEE Transactions on Knowledge and Data Engineering, 27(10), 2015, pp. 2700-2716.
[9] Hajian, S. and Domingo-Ferrer, J., “A methodology for direct and indirect discrimination prevention in data mining.,” in IEEE transactions on knowledge and data engineering, 25(7), 2012, pp. 1445-1459.
[10] Zhang, N. and Zhao, W., “Privacy-preserving data mining systems” in Computer, 40(4), 2007, pp. 52-58.
[11] Musolesi, M., “Big mobile data mining: Good or evil?.” in IEEE Internet Computing, 18(1), 2007, pp.78-81.
[12] Li, Y., Chen, M., Li, Q. and Zhang, W., “Enabling multilevel trust in privacy preserving data mining” in IEEE Transactions on Knowledge and Data Engineering, 24(9),2011, pp.1598-1612.
[13] Feng, M., Zheng, J., Ren, J., Hussain, A., Li, X., Xi, Y. and Liu, Q., “Big data analytics and mining for effective visualization and trends forecasting of crime data,” in IEEE Access, 2013, pp.106111-106123.
[14] Akay, A., Dragomir, A. and Erlandsson, B.E., “A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin.” in IEEE journal of biomedical and health informatics, 19(1),2012, pp.389-396.
[15] Thuraisingham, B., “A primer for understanding and applying data mining..,” in It Professional, 2(1), 2000, pp.28-31.
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2020-01-23
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