Efficiently mining long patterns from databases

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Efficiently mining long patterns from databases

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International Conference on Management of Data archive
Proceedings of the 1998 ACM SIGMOD international conference on Management of data table of contents

Seattle, Washington, United States

Pages: 85 - 93

Year of Publication: 1998

ISBN:0-89791-995-5

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Author

Roberto J. Bayardo, Jr.

IBM Almaden Research Center

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 18, Downloads (12 Months): 197, Citation Count: 184

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ABSTRACT

We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data show that when the patterns are long, our algorithm is more efficient by an order of magnitude or more.

REFERENCES

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