Mining relational patterns from multiple relational tables

In this paper, we present the concept of relational patterns and our approa ch to extract them from multiple relational tables. Relational patterns are analogous to frequent itemsets extracted by the Apriori algorithm [R, Agra wal, H. Mannila, R. Srikant, H. Toivonen, A.I. Verkamo, Advances in Knowled ge Discovery and Data Mining, AAAI Press, 1995.] in the case of a single ta ble. However, for the multiple relational tables, relational patterns captu re co-occurrences of attributes as well as the relationships between these attributes, which are essential to avoid information loss. We describe our experiences from a test-bed implementation of our approach on a real hospit al's discharge abstract database. This process raised issues, which were th en implemented in order to enhance an analyst's ability to explore patterns while preventing high diversity and abundance of available data from blurr ing subtle patterns of interest. Finally, we evaluate the usefulness of rel ational patterns in the context of the discharge abstract data as well in o ther possible domains. (C) 1999 Elsevier Science B.V. All rights reserved.