DRIMS: A Software Tool to Incrementally Maintain Previous Discovered Rules.

A wide spectrum of methods for knowledge extraction have been proposed up to date. These expensive algorithms become inexact when new transactions are made into business data, an usual problem in real-world applications. The incremental maintenance methods arise to avoid reruns of those algorithms from scratch by reusing information that is systematically maintained. This paper introduces a software tool: Data Rules Incremental Maintenance System (DRIMS) which is a free tool written in Java for incrementally maintain three types of rules: association rules, approximate dependencies and fuzzy association rules. Several algorithms have been implemented in this tool for relational databases using their active resources. These algorithms are inspired in efficient computation of changes and do not include any mining technique. We operate on discovered rules in their final form and sustain measures of rules up-to-date, ready for real-time decision support. Algorithms are applied over a generic form of measures allowing the maintenance of a wide rules' metrics in an efficient way. DRIMS software tool do not discover new knowledge, it has been designed to efficiently maintain interesting information previously extracted.