A Survey of Methodologies and Techniques for Data Mining and Intelligent Data Discovery
This paper gives a description of data mining and its methodology. First, the definition of data mining along with the purposes and growing needs for such a technology are presented. A six-step methodology for data mining is then presented and discussed. The goals and methods of this process are then explained, coupled with a presentation of a number of techniques that are making the data mining process faster and more reliable. These techniques include the use of neural networks and genetic algorithms, which are presented and explained as a way to overcome several complexity problems that the data mining process possesses. A deep survey of the literature is done to show the various purposes and achievements that these techniques have brought to the study of data mining.
KeywordsGenetic Algorithm Data Mining Data Mining Algorithm Structure Query Language Learning Classifier System
Unable to display preview. Download preview PDF.
- Adriaans, P. and D. Zantinge, Data Mining, Harlow: Addison-Wesley, 1996.Google Scholar
- Fayyad, U., D. Madigan, G. Piatetsky-Shapiro and P. Smyth, “From data mining to knowledge discovery in databases,” AI Magazine, 17: 37–54, 1996.Google Scholar
- Hand, D.J., “Data mining: statistics and more?,” The American Statistician, 52: 112–118, 1998.Google Scholar
- Heckerman, D., “Bayesian Networks for Knowledge Discovery,” Advances in Knowledge Discovery and Data Mining, AAAI Press, 273–305, 1996.Google Scholar
- Koonce, D.A., C. Fang and S. Tsai, “A data mining tool for learning from manufacturing systems,” Computers & Industrial Engineering, 33: 27–30, 1997Google Scholar
- Kusiak, A., Computational Intelligence in Design and Manufacturing. Wiley-Interscience Publications, pp. 498–526, 1999.Google Scholar
- Yevich, R., “Data Mining,” in Data Warehouse: Practical Advice from the Experts, pp. 309–321, Prentice Hall, 1997.Google Scholar