SET-ORIENTED INDEXES FOR DATA MINING QUERIES

Abstract: One of the most popular data mining methods is frequent itemset and association rule discovery. Mined patterns are usually stored in a relational database for future use. Analyzing discovered patterns requires excessive subset search querying in large amount of database tuples. Indexes available in relational database systems are not well suited for this class of queries. In this paper we study the performance of four different indexing techniques that aim at speeding up data mining queries, particularly improving set inclusion queries in relational databases. We investigate the performance of those indexes under varying factors including the size of the database, the size of the query, the selectivity of the query, etc. Our experiments show significant improvements over traditional database access methods using standard B+ tree indexes.