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AUTHOR(S):
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TITLE Exploring Practical Data Mining Techniques at Undergraduate Level |
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ABSTRACT Data mining is referred to as the process of analyzing and extracting patterns embedded in large amounts of data by using various methods from machine learning, pattern recognition, statistics and database management. With the rapid proliferation of the Internet and advances of computing technology, data mining has become an increasingly important tool of transforming large quantities of digital data into previously unknown and meaningful information and has been applied in many areas that include business and finance, health care, telecommunication, science and higher education. Data mining is also a relatively new field of computer science, and there are only a few undergraduate data mining courses are currently offered in institutions of higher education. In this paper, we describe the design, implementation and evaluation of a data mining course that we have developed and offered as an undergraduate computer science upper-division elective at the University of San Diego. The course combines lectures on a number of key data mining principles and applications, mini student lecture sessions, programming projects and research activities to engage students in active learning. Our experience has shown that data mining can be taught successfully at the undergraduate level and students can learn a great deal of data mining techniques and are able to apply them to solve many real world problems. |
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KEYWORDS Data mining, machine learning, computer science curriculum, classification, clustering, association analysis, anomaly detection |
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REFERENCES [1] Antonie M, Zaiane, O. and Coman, A., Application of Data Mining Techniques for Medical Image Classification, Proceedings of the 2nd International Workshop on Multimedia Data Mining, 2001. [2] Ayres, I., Super Crunchers, Random House, 2008. [3] Chawla, N., Teaching Data Mining by Coalescing Theory and Applications, Proceedings of the 35th ASEE/IEEE Frontiers in Education Conference, 2005. [4] Data Mining Cup 2011, http://www.datamining-cup.de/en/dmc-competition. [5] Lopez, D. and Ludwig, L, Data Mining at the Undergraduate Level, Proceedings of the Midwest Instruction and Computing Symposium, 2001. [6] Musicant, D., A Data Mining Course for Computer Science: Primary Sources and Implementations, Proceedings of SIGCSE, 2006. [7] Sequer, J., A Data Mining Course for Computer Science and Non-Computer Science Students, Journal of Computer Sciences in Colleges, Vol.22, No.4, 2007, pp. 109-114. [8] Tan, P., Steinbach, M. and Kumar, V., Introduction to Data Mining, Addison Wesley, 2006. [9] University of California at Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/. [10] Witten, I. and Frank, E., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2nd Edition, 2005. |
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Cite this paper Eric P. Jiang. (2016) Exploring Practical Data Mining Techniques at Undergraduate Level. International Journal of Computers, 1, 76-82 |
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