A Comparison and Scenario Analysis of Leading Data Mining Software

A Comparison and Scenario Analysis of Leading Data Mining Software

John Wang (Montclair State University, USA), Xiaohua Hu (Drexel University, USA), Kimberly Hollister (Montclair State University, USA) and Dan Zhu (Iowa State University, USA)
DOI: 10.4018/978-1-60566-092-9.ch012
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Abstract

Finding the right software is often hindered by different criteria as well as by technology changes. We performed an analytic hierarchy process (AHP) analysis using Expert Choice to determine which data mining package was best suitable for us. Deliberating a dozen alternatives and objectives led us to a series of pair-wise comparisons. When further synthesizing the results, Expert Choice helped us provide a clear rationale for the decision. The issue is that data mining technology is changing very rapidly. Our article focused only on the major suppliers typically available in the market place. The method and the process that we have used can be easily applied to analyze and compare other data mining software or knowledge management initiatives.
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Data Mining Software

Data mining software analyzes- based on open-ended user queries- relationships and patterns that are stored in transaction data. Available are several types of analytical software: statistical, machine learning and neural networks, decision trees, Naive-Bayes, K-Nearest Neighbor, rule induction, clustering, rules based, linear and logistical regression time sequence, and so forth. Along the lines of Mena (1998) and Martin (2005), the basic steps of data mining for knowledge discoveries are:

  • 1.

    Define business problem

  • 2.

    Build data mining data base

  • 3.

    Explore data

  • 4.

    Prepare data for modeling

  • 5.

    Build model

  • 6.

    Evaluate model

  • 7.

    Deploy model

  • 8.

    Results

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