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Abstract: Association rules are statements of the form "for 90 % of the rows of the relation, if the row has value 1 in the columns in set W , then it has 1 also in column B". Agrawal, Imielinski, and Swami introduced the problem of mining association rules from large collections of data, and gave a method based on successive passes over the database. We give an improved algorithm for the problem. The method is based on careful combinatorial analysis of the information obtained in previous passes; this... (Update)
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BibTeX entry: (Update)
H. Mannila, H. Toivonen, and I. Verkamo. Efficient algorithms for discovering association rules. In AAAI Wkshp. Knowledge Discovery in Databases, July 1994. http://citeseer.ist.psu.edu/mannila94efficient.html More
@inproceedings{ mannila94efficient,
author = "Heikki Mannila and Hannu Toivonen and A. Inkeri Verkamo",
title = "Efficient algorithms for discovering association rules",
booktitle = "{AAAI} Workshop on Knowledge Discovery in Databases ({KDD}-94)",
publisher = "AAAI Press",
address = "Seattle, Washington",
editor = "Usama M. Fayyad and Ramasamy Uthurusamy",
pages = "181--192",
year = "1994",
url = "citeseer.ist.psu.edu/mannila94efficient.html" }
Citations (may not include all citations):
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The Probabilistic Method (context) - Alon, Spencer - 1992 ACM DBLP
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Mining association rules between sets of items in large data..
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Fast algorithms for mining association rules in large databa.. (context) - Agrawal, Srikant - 1994 ACM DBLP
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Information Processing Letters (context) - Hagerup, Rub et al. - 1989
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Cambridge University Press (context) - Bollob'as - 1986
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Data Structures and Algorithms (context) - Mehlhorn - 1984 ACM
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Max-Planck-Institut fur Informatik (context) - Naher, manual et al. - 1992
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Association rules in sequential data (context) - Mannila, Toivonen et al. - 1994
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