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.
Keywords
Computer scienceData miningA priori and a posterioriApriori algorithmScale (ratio)Exponential growthDatabaseAlgorithmPattern recognition (psychology)Association rule learningArtificial intelligenceMathematicsGeography
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Publication Info
- Year
- 1998
- Type
- article
- Pages
- 85-93
- Citations
- 1297
- Access
- Closed
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Cite This
Roberto J. Bayardo
(1998).
Efficiently mining long patterns from databases.
, 85-93.
https://doi.org/10.1145/276304.276313
Identifiers
- DOI
- 10.1145/276304.276313