Classification and Positioning of Data Mining Tools

Abstract

Various models for the KDD (Knowledge Discovery in Databases) process are known, which mainly differ with respect to the number and description of process activities. We present a process unification by assigning the single steps of these models to five main stages and concentrate on data mining aspects. An overview concerning data mining software tools with focus on inbuilt algorithms and additional support provided for the main stages of the KDD process is given within a classification and positioning framework. Finally, an application of a modification of an association rule algorithm is used as empirical example to demonstrate what can be expected when data mining tools are used to handle large data sets.