A Complete Study on the Challenges in Mining Large Amount of Mixed-Mode Data
The data mining is helping in many fields to analyze vast data in an efficient manner. As the amount of data that are available in the real world problems to be dealt with is very large the importance of data mining is also increasing. The various schemes in which a data can be analyzed and handled are statistical analysis and data mining. The data mining is actually suitable when the size of the data set is large. There are different ways in handling the data set. But if the data set which is picked up is a mixed data set then the problems will also be more in performing the classification and also to form a pattern. Hence the general study on data sets and the mixed mode data sets are done so that the problems faced while mining the data can be dealt with in an efficient manner.
“Handbook of statistical analysis and data mining applications”, John F. Elder IV, Bob Nisbet, Gary Miner, 2009, ISBN 978-0-12-374765-5.
“Ensemble methods in Data Mining: Improving accuracy through combining predictions”, John F. Elder IV, Giovanni Seni, February 2010, Morgon & Clypool.
“Three perspectives of data mining”, Zhi-Hua Zhou, Artificial Intelligence, 2003, 143(1):139-146.
”Data Mining and Business Analytics”, Johannes Ledolter, Wiley Publication, 2013
“Teaching Computational Thinking In Probability Using SpreadSheet Simulation”, Anand. R, Manju. M, Anju M. Kaimal, Veenaa Deeve NV, Chithra R, International Journal of Scientific and Research Publications, ISSN 2250-3153
“New Trends in Data Mining”, J. Huysmans, B. Baesens, D. Martens, K. Denys, J. Vanthienen, Vol I, 4, 2005
WH Au, KCC Chan, AKC Wong and Y Wang, “Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data” IEEE/ACM Trans on Computational Biology and Bioinformatics, Vol 2, No2, pp 83-101, 2005.
A. K. C. Wong and D. K. Y. Chiu, “Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 9, no. 8, pp. 796-805, 1987
C. C. Wang and A. K. C. Wong, “Classification of Discrete-Valued Data with Feature Space Transformation,” IEEE Trans. on Automatic Control, vol. AC-24, no. 3, pp. 434–437, 1979
Wai-Ho Au, Keith C.C. Chan, Andrew K.C. Wong, and Yang Wang, Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data, IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 2, NO. 2, APRIL - JUNE 2005”.
Quinlan J.R. C4.5:programs for Machine Learning. Morgan Kaufmann, 1993. Tou J.T and Gonzalez R.C. Pattern Recognition Principles. Addison-Wesley, 1974
A.K.C. Wong, D.K.Y. Chiu and W. Huang, ‘A Discrete-Valued Clustering Algorithm with Applications to Bimolecular Data,’ Information Sciences, vol. 139, pp. 97-112, 2002.
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