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A scalable bottom-up data mining algorithm for relational databases

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3 Author(s)
Giuffrida, G. ; Dept. of Comput. Sci., California Univ., Los Angeles, CA, USA ; Cooper, L.G. ; Chu, W.W.

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

Published in:

Scientific and Statistical Database Management, 1998. Proceedings. Tenth International Conference on

Date of Conference:

1-3 Jul 1998