Open AccessProceedings Article
Sampling Large Databases for Association Rules
Hannu Toivonen
- pp 134-145
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TLDR
New algorithms that reduce the database activity considerably by picking a Random sample, to find using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database.Abstract:
Discovery of association rules .is an important database mining problem. Current algorithms for finding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very significant for very large databases. We present new algorithms that reduce the database activity considerably. The idea is to pick a Random sample, to find using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database. The algorithms thus produce exact association rules, not approximations based on a sample. The approach is, however, probabilistic, and in those rare cases where our sampling method does not produce all association rules, the missing rules can be found in a second pass. Our experiments show that the proposed algorithms can find association rules very efficiently in only one databaseread more
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References
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Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
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Fast Algorithms for Mining Association Rules in Large Databases
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