Discovery of Multiple-Level Association Rules from Large Databases

With the widespread computerization in business, government, and science, the efficient and effective discovery of interesting information from large databases becomes essential. Data mining or Knowledge Discovery in Database (KDD) emerges as a solution to the data analysis problems faced by many organizations. Previous studies on data mining have been focused on the discovery of knowledge at a single conceptual level, either at the primitive level or at a rather high conceptual level. However, it is often desirable to discover knowledge at multiple conceptual levels, which will provide a spectrum of understanding, from general to specific, for the underlying data. In this thesis, we first introduce the conceptual hierarchy, a hierarchical organization of the data in the databases. Two algorithms for dynamic adjustment of conceptual hierarchies are developed, as well as another algorithm for automatic generation of conceptual hierarchies for numerical attributes. In addition, a set of algorithms is developed for mining multiple-level characteristic, discriminant and association rules. All algorithms developed were implemented and tested in our data mining prototype system, DBMiner. The attribute-oriented induction method is extended to discover multiple-level characteristic and discriminant rules. A progressive deepening method is proposed for mining multiple-level association rules. Several variants of the method with different optimization techniques are implemented and tested. The results show the method is efficient and effective. Furthermore, a new approach to association rule mining, meta-rule guided mining, is proposed. The experiments show that meta-rule guided mining is powerful and efficient. Finally, an application of data mining techniques, cooperative query answering using multiple layered databases, is presented. Our study concludes that mining knowledge at multiple levels is both practical and desirable, and thus is an interesting research direction. Some future research problems are also discussed.