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Dynamic Itemset Counting and Implication Rules for Market Basket Data (1997)  (Make Corrections)  (242 citations)
Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur
SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA



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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... (Update)

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BibTeX entry:   (Update)

Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, and Shalom Tsur. Dynamic itemset counting and implication rules for market basket data. SIGMOD Record (ACM Special Interest Group on Management of Data), 26(2):255, 1997. http://citeseer.ist.psu.edu/brin97dynamic.html   More

@inproceedings{ brin97dynamic,
    author = "Sergey Brin and Rajeev Motwani and Jeffrey D. Ullman and Shalom Tsur",
    title = "Dynamic itemset counting and implication rules for market basket data",
    booktitle = "SIGMOD 1997, Proceedings ACM SIGMOD International Conference
               on Management of Data, May 13-15, 1997, Tucson, Arizona, USA",
    month = "05",
    publisher = "ACM Press",
    editor = "Joan Peckham",
%    isbn = "?",
    pages = "255--264",
    year = "1997",
    url = "citeseer.ist.psu.edu/brin97dynamic.html",
    url = "citeseer.nj.nec.com/brin97dynamic.html" }
Citations (may not include all citations):
921   Mining Association Rules between Sets of Items in Large Data.. - Agrawal, Imilienski et al. - 1993  ACM   DBLP
910   Fast algorithms for mining association rules - Agrawal, Srikant - 1994
340   Mining sequential patterns - Agrawal, Srikant - 1995  ACM   DBLP
268   Mining generalized association rules - Srikant, Agrawal - 1995  ACM   DBLP
189   Sampling large databases for association rules - Toivonen - 1996
126   Fast similarity search in the presence of noise (context) - Agrawal, Lin et al. - 1995
117   IEEE Transactions on Knowledge and Data Engineering (context) - Agrawal, Imilienski et al. - 1993
111   Sliq: A fast scalable classifier for data mining - Mehta, Agrawal et al. - 1996  DBLP



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