A scalable bottom-up data mining algorithm for relational databases

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 datab...

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Bibliographic Details
Published inProceedings. Tenth International Conference on Scientific and Statistical Database Management (Cat. No.98TB100243) pp. 206 - 209
Main Authors Giuffrida, G., Cooper, L.G., Chu, W.W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1998
Subjects
Classification algorithms
Data mining
Indexing
Induction generators
Law
Machine learning
Operating systems
Relational databases
Spatial databases
Testing
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ISBN0818685751
9780818685750
ISSN1099-3371
DOI10.1109/SSDM.1998.688125

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Summary: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.
ISBN:0818685751
9780818685750
ISSN:1099-3371
DOI:10.1109/SSDM.1998.688125