Sergey Brin
Abstract
We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating “implication rules,” which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed by synthetic data, can dramatically affect the performance of the system and the form of the results.
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Digital Library
- AIS93b R. Agrawal, T. Imilienski, and A. Swami. Mining Association Rules between Sets of Items in Large Databases. Proc. of the A CM SIGMOD Int'l Conf. on Management of Data, pages 207- 216, May 1993. Google ScholarDigital Library
- ALSS95 R. Agrawal, K. Lin, S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling and translation in time-series databases. in Proc. of the Int'l Conf. on Very Large Data Bases (VLDB), 1995. Google ScholarDigital Library
- AS94 R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994. Google ScholarDigital Library
- AS95 R. Agrawal and R. Srikant. Mining sequential patterns. In Proceedings of the l lth International Conm}erence on Data Engineering, Taipei, Taiwan, 1995. Google ScholarDigital Library
- MAR96 M. Mehta, R. Agrawal, and J. Rissanen. Sliq: A fast scalable classifier for data mining. March 1996.Google Scholar
- SA95 R. Srikant and R. Agrawal. Mining generalized association rules. 1995.Google Scholar
- Toi96 H. Toivonen. Sampling large databases for association rules. Proc. of the Int'l Conf. on Very Large Data Bases (VLDB), 1996. Google ScholarDigital Library
Index Terms
- Dynamic itemset counting and implication rules for market basket data
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Dynamic itemset counting and implication rules for market basket data
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