A scalable bottom-up data mining algorithm for relational databases

Abstract

Machine learning induction algorithms are difficult to scale to very large databases because of their memory-bound nature. Using virtual memory results in a significant performance degradation. To overcome such shortcomings, we developed a classification rule induction algorithm for relational databases. Our algorithm uses a bottom-up rule generation strategy that is more effective for mining databases having large cardinality of nominal variables. We have successfully used our algorithm to mine a retail grocery database containing more than 1.6 million records in about 5 hours on a dual Pentium processor PC.