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Summary and Conclusions
The fundamental techniques used for data mining can be classified into distinct groups, each offering advantages and trade-offs. The modern techniques rely on pattern distillation, rather than data retention. Pattern distillation can be classified into logical, equational and cross-tabulational methods. The underlying structure of these approaches was discussed and compared. Hybrid approaches are likely to succeed best, merging logic and equations with multidimensional analysis. However, the over structure of how these techniques are used should be viewed in the context of machine-man interaction (Parsaye, 1997).
References
Tukey, J. Exploratory Data Analysis, New York: McMillan, 1973.
Parsaye, K., OLAP and Data Mining: Bridging the Gap. Database Programming & Design, February 1997.
Parsaye, K., and M.H. Chignell: Intelligent Database Tools and Applications. New York: John Wiley and Sons, 1993 .
Parsaye, K., Data Mining with OLAP Affinities, to be published.
Parsaye, K., New Realms of Analysis. Database Programming & Design, September 1996. |
