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A Review of Software Packages for Data Mining

Dominique Haughton, Joel Deichmann, Abdolreza Eshghi, Selin Sayek, Nicholas Teebagy and Heikki Topi
The American Statistician
Vol. 57, No. 4 (Nov., 2003), pp. 290-309
Stable URL: http://www.jstor.org/stable/30037299
Page Count: 20
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A Review of Software Packages for Data Mining
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Abstract

We present to the statistical community an overview of five data mining packages with the intent of leaving the reader with a sense of the different capabilities, the ease or difficulty of use, and the user interface of each package. We are not attempting to perform a controlled comparison of the algorithms in each package to decide which has the strongest predictive power, but instead hope to give an idea of the approach to predictive modeling used in each of them. The packages are compared in the areas of descriptive statistics and graphics, predictive models, and association (market basket) analysis. As expected, the packages affiliated with the most popular statistical software packages (SAS and SPSS) provide the broadest range of features with remarkably similar modeling and interface approaches, whereas the other packages all have their special sets of features and specific target audiences whom we believe each of the packages will serve well. It is essential that an organization considering the purchase of a data mining package carefully evaluate the available options and choose the one that provides the best fit with its particular needs.

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