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[2] R. Vrbić, “Data mining and cloud computing,†Journal of Information Technology & Applications, Vol. 2, No. 2, pp. 75-87, 2012.
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[4] A. Bifet, “Mining Big Data in Real Time,†Informatica, Vol.37, pp. 15–20, 2013.
[5] G. Krempl, I. Zliobaite, D. B. Nski, E. H. Ullermeier, et. al., “Open Challenges for Data Stream Mining Research,†ACM SIGKDD Explorations, Vol. 16, No. 1, pp. 1-10, 2013.
[6] D.-H. Tran, M. M. Gaber, K.-U. Sattler, “Change detection in streaming data in the era of big data: models and issues,†ACM SIGKDD Explorations, Vol. 16, No. 1, pp. 30-38, 2014.
[7] W. Fan, A. Bifet, “Mining Big Data: Current Status, and Forecast to the Future,†ACM SIGKDD Explorations, Vol. 14, No. 2, pp. 1-5, December 2012.
[8] Y. Demchenko, P. Grosso, C. D. Laat, P. Membrey, “Addressing Big Data Issues in Scientific Data Infrastructure,†2013 International Conference on Collaboration Technologies and Systems (CTS), 20-24 May 2013, San Diego, CA, USA, pp. 48-55, 2013.
[9] D.E. O'Leary, “'Big Data', the 'Internet of Things' and the 'Internet of Signs',†Intelligent Systems in Accounting, Finance and Management, Vol. 20, pp. 53-65, 2013.
[10] H.V. Jagadish, A. Labrinidis, Y. Papakonstantinou, et al., “Big Data and Its Technical Challenges,†Communications of the ACM, Vol. 57, No. 7, pp. 86-94, 2014.
[11] S. K. Markham, M. Kowolenko, and T. L. Michaelis, “Unstructured Text Analytics to Support New Product Development Decisions,†Research Technology Management, pp. 30-38, March-April, 2015.
[12] S. B. Boddu, “Eliminate the noisy data from web pages using data mining techniques,†Computer Science and Telecommunications, Vol. 38, No. 2, pp. 39-46, 2013.
[13] C.L. P. Chen, C.-Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,†Information Sciences, Vol. 275, No. 10, pp. 314-347, 2014.
[14] M. Chen, S.-W. Mao, Y.-H. Liu, “Big data: A survey,†Mobile Netw Appl, Vol. 19, pp. 171-209, 2014.
[15] K. M. Lee, “Grid-based Single Pass Classification for Mixed Big Data,†International Journal of Applied Engineering Research, Vol. 9, No. 21, pp. 8737-8746, 2014.
[16] N. Karacapilidis, M. Tzagarakis and S. Christodoulou, “On a meaningful exploitation of machine and human reasoning to tackle data-intensive decision making,†Intelligent Decision Technologies, Vol. 7, pp. 225–236, 2013.
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[20] S. Londhea, S. Mahajan, “New Approach For Big Data Mining Using MapReduce Techniques,†International Journal of Applied Engineering Research, Vol. 10, No. 6, pp. 15407-15415, 2015.
[21] X. Wu, X. Zhu, G.-Q. Wu, W. Ding, “Data Mining with Big Data,†Knowledge and Data Engineering, IEEE Transactions on, Vol. 26, No. 1, pp. 97-107, 2013.
[22] K. Pal, J. Saini, “A Study of Current State of Work and Challenges in Mining Big Data,†International Journal of Advanced Networking Applications, Special Issue, pp. 73-76, 2014.
[23] N.N.R. R. Suri, M. N. Murty and G. Athithan, “A ranking-based algorithm for detection of outliers in categorical data,†International Journal of Hybrid Intelligent Systems, Vol. 11, pp. 1–11, 2014.
[24] J. Vaidya, B. Shafiq, W. Fan, D. Mehmood, and D. Lorenzi, “A Random Decision Tree Framework for Privacy-Preserving Data Mining,†IEEE Transactions on Dependable and Secure Computing, Vol. 11, No. 5, pp. 399-411, 2014.
[25] K. M. Ting, T. Washio, J. R. Wells, F. T. Liu, S. Aryal, “DEMass: a new density estimator for big data,†Knowledge & Information Systems, Vol. 35, pp. 493–524, 2013.
[26] M. Herland, T. M Khoshgoftaar and R. Wald, “A review of data mining using big data in health informatics,†Journal of Big Data, Vol. 1, No. 2, pp. 1-35, 2014.
[27] S. Moens, E. Aksehirli and B. Goethals, “Frequent Itemset Mining for Big Data,†2013 IEEE International Conference on Big Data, 6-9 Oct. 2013, Silicon Valley, CA, USA, PP. 111- 118.
[28] F. Ryohei, M. Satoshi, “The Most Advanced Data Mining of the Big Data Era,†NEC Technical Journal, Vol.7 No.2, PP. 91-95, 2012.
[29] S. C.H. Hoi, J. Wang, P. Zhao, R. Jin, “Online Feature Selection for Mining Big Data,†Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, PP. 93-100, 2012.
[30] U. Jaswant and P.N. Kumar, “Big Data Analytics: A Supervised Approach for Sentiment Classification Using Mahout: An Illustration,†International Journal of Applied Engineering Research, Vol. 10, No. 5, pp. 13447-13457, 2015.
[31] V.J. Nirmal and D.I.G. Amalarethinam, “Parallel Implementation of Big Data Pre-Processing Algorithms for Sentiment Analysis of Social Networking Data,†International Journal of Fuzzy Mathematical Archive, Vol. 6, No. 2, pp.149-159, 2015.
[2] R. Vrbić, “Data mining and cloud computing,†Journal of Information Technology & Applications, Vol. 2, No. 2, pp. 75-87, 2012.
[3] V. Nekvapil, “Cloud computing in data mining – a survey,†Journal of Systems Integration, No. 1, pp. 12-23, 2015.
[4] A. Bifet, “Mining Big Data in Real Time,†Informatica, Vol.37, pp. 15–20, 2013.
[5] G. Krempl, I. Zliobaite, D. B. Nski, E. H. Ullermeier, et. al., “Open Challenges for Data Stream Mining Research,†ACM SIGKDD Explorations, Vol. 16, No. 1, pp. 1-10, 2013.
[6] D.-H. Tran, M. M. Gaber, K.-U. Sattler, “Change detection in streaming data in the era of big data: models and issues,†ACM SIGKDD Explorations, Vol. 16, No. 1, pp. 30-38, 2014.
[7] W. Fan, A. Bifet, “Mining Big Data: Current Status, and Forecast to the Future,†ACM SIGKDD Explorations, Vol. 14, No. 2, pp. 1-5, December 2012.
[8] Y. Demchenko, P. Grosso, C. D. Laat, P. Membrey, “Addressing Big Data Issues in Scientific Data Infrastructure,†2013 International Conference on Collaboration Technologies and Systems (CTS), 20-24 May 2013, San Diego, CA, USA, pp. 48-55, 2013.
[9] D.E. O'Leary, “'Big Data', the 'Internet of Things' and the 'Internet of Signs',†Intelligent Systems in Accounting, Finance and Management, Vol. 20, pp. 53-65, 2013.
[10] H.V. Jagadish, A. Labrinidis, Y. Papakonstantinou, et al., “Big Data and Its Technical Challenges,†Communications of the ACM, Vol. 57, No. 7, pp. 86-94, 2014.
[11] S. K. Markham, M. Kowolenko, and T. L. Michaelis, “Unstructured Text Analytics to Support New Product Development Decisions,†Research Technology Management, pp. 30-38, March-April, 2015.
[12] S. B. Boddu, “Eliminate the noisy data from web pages using data mining techniques,†Computer Science and Telecommunications, Vol. 38, No. 2, pp. 39-46, 2013.
[13] C.L. P. Chen, C.-Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,†Information Sciences, Vol. 275, No. 10, pp. 314-347, 2014.
[14] M. Chen, S.-W. Mao, Y.-H. Liu, “Big data: A survey,†Mobile Netw Appl, Vol. 19, pp. 171-209, 2014.
[15] K. M. Lee, “Grid-based Single Pass Classification for Mixed Big Data,†International Journal of Applied Engineering Research, Vol. 9, No. 21, pp. 8737-8746, 2014.
[16] N. Karacapilidis, M. Tzagarakis and S. Christodoulou, “On a meaningful exploitation of machine and human reasoning to tackle data-intensive decision making,†Intelligent Decision Technologies, Vol. 7, pp. 225–236, 2013.
[17] M. Turk, A chart of the big data ecosystem, take 2, 2012. http://mattturck.com/2012/10/15/a-chart-of-the-big-data-ecosystem-take-2/
[18] J. Dean, “Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners,†John Wiley & Sons, Inc., 2014.
[19] Y. Zhang, D. Sow, D. Turaga, M. v. d. Schaar, “A Fast Online Learning Algorithm for Distributed Mining of BigData,†ACM SIGMETRICS Performance Evaluation Review, Vol. 41, No. 4, pp. 90-93, 2014.
[20] S. Londhea, S. Mahajan, “New Approach For Big Data Mining Using MapReduce Techniques,†International Journal of Applied Engineering Research, Vol. 10, No. 6, pp. 15407-15415, 2015.
[21] X. Wu, X. Zhu, G.-Q. Wu, W. Ding, “Data Mining with Big Data,†Knowledge and Data Engineering, IEEE Transactions on, Vol. 26, No. 1, pp. 97-107, 2013.
[22] K. Pal, J. Saini, “A Study of Current State of Work and Challenges in Mining Big Data,†International Journal of Advanced Networking Applications, Special Issue, pp. 73-76, 2014.
[23] N.N.R. R. Suri, M. N. Murty and G. Athithan, “A ranking-based algorithm for detection of outliers in categorical data,†International Journal of Hybrid Intelligent Systems, Vol. 11, pp. 1–11, 2014.
[24] J. Vaidya, B. Shafiq, W. Fan, D. Mehmood, and D. Lorenzi, “A Random Decision Tree Framework for Privacy-Preserving Data Mining,†IEEE Transactions on Dependable and Secure Computing, Vol. 11, No. 5, pp. 399-411, 2014.
[25] K. M. Ting, T. Washio, J. R. Wells, F. T. Liu, S. Aryal, “DEMass: a new density estimator for big data,†Knowledge & Information Systems, Vol. 35, pp. 493–524, 2013.
[26] M. Herland, T. M Khoshgoftaar and R. Wald, “A review of data mining using big data in health informatics,†Journal of Big Data, Vol. 1, No. 2, pp. 1-35, 2014.
[27] S. Moens, E. Aksehirli and B. Goethals, “Frequent Itemset Mining for Big Data,†2013 IEEE International Conference on Big Data, 6-9 Oct. 2013, Silicon Valley, CA, USA, PP. 111- 118.
[28] F. Ryohei, M. Satoshi, “The Most Advanced Data Mining of the Big Data Era,†NEC Technical Journal, Vol.7 No.2, PP. 91-95, 2012.
[29] S. C.H. Hoi, J. Wang, P. Zhao, R. Jin, “Online Feature Selection for Mining Big Data,†Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, PP. 93-100, 2012.
[30] U. Jaswant and P.N. Kumar, “Big Data Analytics: A Supervised Approach for Sentiment Classification Using Mahout: An Illustration,†International Journal of Applied Engineering Research, Vol. 10, No. 5, pp. 13447-13457, 2015.
[31] V.J. Nirmal and D.I.G. Amalarethinam, “Parallel Implementation of Big Data Pre-Processing Algorithms for Sentiment Analysis of Social Networking Data,†International Journal of Fuzzy Mathematical Archive, Vol. 6, No. 2, pp.149-159, 2015.
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Affiliations
Lidong Wang
Mississippi Valley State University, USA
Guanghui Wang
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences
Data Mining Applications in Big Data
Abstract
Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.