Mining Generalized Association Rules.

Ramakrishnan Srikant, Rakesh Agrawal: Mining Generalized Association Rules. VLDB 1995: 407-419

@inproceedings{DBLP:conf/vldb/SrikantA95,
  author    = {Ramakrishnan Srikant and
               Rakesh Agrawal},
  editor    = {Umeshwar Dayal and
               Peter M. D. Gray and
               Shojiro Nishio},
  title     = {Mining Generalized Association Rules},
  booktitle = {VLDB'95, Proceedings of 21th International Conference on Very
               Large Data Bases, September 11-15, 1995, Zurich, Switzerland},
  publisher = {Morgan Kaufmann},
  year      = {1995},
  isbn      = {1-55860-379-4},
  pages     = {407-419},
  ee        = {db/conf/vldb/SrikantA95.html},
  crossref  = {DBLP:conf/vldb/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists ofa set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy that says that jackets is-a outerwearis-a clothes, we may infer a rule that "people who buy outerwear tend to buy shoes". This rule may hold even if rules that "people who buy jackets tend to buy shoes", and "people who buy clothes tend to buy shoes" do not hold. An obvious solution to the problem is to add all ancestors of each item ina transaction to the transaction, and then run any of the algorithms for mining association rules on these "extended transactions". However, this "Basic" algorithm is not very fast; we present two algorithms, Cumulate and EstMerge, which run 2 to 5 times faster than Basic (and more than 100 times faster on one real-life dataset). We also present a new interest-measure for rules which uses the information in the taxonomy. Given a user-specified "minimum-interest-level", this measure prunes a large number of redundant rules; 40% to 60% of all the rules were pruned on two real-life datasets.

Copyright © 1995 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Printed Edition

Umeshwar Dayal, Peter M. D. Gray, Shojiro Nishio (Eds.): VLDB'95, Proceedings of 21th International Conference on Very Large Data Bases, September 11-15, 1995, Zurich, Switzerland. Morgan Kaufmann 1995, ISBN 1-55860-379-4
Contents

References

[1]: Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216
[2]: Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499
[3]: Noga Alon, Joel Spencer: The Probabilistic Method. John Wiley 1992, ISBN 0-471-53588-5
Contents
[4]: Torben Hagerup, Christine Rüb: A Guided Tour of Chernoff Bounds. Inf. Process. Lett. 33(6): 305-308(1990)
[5]: Maurice A. W. Houtsma, Arun N. Swami: Set-Oriented Mining for Association Rules in Relational Databases. ICDE 1995: 25-33
[6]: Heikki Mannila, Hannu Toivonen, A. Inkeri Verkamo: Efficient Algorithms for Discovering Association Rules. KDD Workshop 1994: 181-192
[7]: Jong Soo Park, Ming-Syan Chen, Philip S. Yu: An Effective Hash Based Algorithm for Mining Association Rules. SIGMOD Conference 1995: 175-186
[8]: Gregory Piatetsky-Shapiro: Discovery, Analysis, and Presentation of Strong Rules. Knowledge Discovery in Databases 1991: 229-248
[9]: Ramakrishnan Srikant, Rakesh Agrawal: Mining Generalized Association Rules. VLDB 1995: 407-419