On decision tree induction for knowledge discovery in very large databases

Knowledge Discovery in Databases is the process of extracting new patterns from existing data. Decision Tree Induction is the process of creating decision trees from samples of data and validating them for the whole data base. The approach taken in this project uses decision trees not just for solving the classification problem in Knowledge Discovery; but for forming association rules from them which are in effect new and explicit knowledge. Several performance problems need to be addressed for using a decision tree approach to large scale databases. I offer a new criterion which is better suited to decision tree construction and its mapping to association rules. The emphasis is on efficient, incremental, and parallel algorithms as effective ways to deal with large amounts of data. Comparisons with existent systems are shown to illustrate the applicability of the solution described in this dissertation to the problem of finding rules (knowledge discovery) and classifying data in very large databases.