Knowledge discovery in databases

Advances in computing hardware and software, especially in the areas of data capture and storage, have resulted in the generation of more data than can be analyzed. Most applications do not consider the patterns and relationships that exist in data external to their domain. There is a need to evolve ways and means to extract knowledge from large databases. The approaches that have been tried so far include intelligent databases, expert databases, deductive databases and data mining. This research introduces and develops the Heuristic Data Mining Model (HDMM). It uses a heuristic rather than cognitive approach to data mining. The HDMM is less computationally intensive and is more intuitive as compared to its cognitive counterparts. The design, implementation and testing of a prototype based on the HDMM are presented. The prototype uses a relational database management system (RDBMS) making it cost effective and practical, considering the popularity of RDBMSs.