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A New Framework For Itemset Generation.

Charu C. Aggarwal, Philip S. Yu: A New Framework For Itemset Generation. PODS 1998: 18-24
@inproceedings{DBLP:conf/pods/AggarwalY98,
  author    = {Charu C. Aggarwal and
               Philip S. Yu},
  title     = {A New Framework For Itemset Generation},
  booktitle = {Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium
               on Principles of Database Systems, June 1-3, 1998, Seattle, Washington},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-996-3},
  pages     = {18-24},
  ee        = {http://doi.acm.org/10.1145/275487.275490, db/conf/pods/AggarwalY98.html},
  crossref  = {DBLP:conf/pods/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

The problem of finding association rules in a large database of sales transactions has been widely studied in the literature. We discuss some of the weaknesses of the large itemset method for association rule generation. A different method for evaluating and finding itemsets referred to as strongly collective itemsets is proposed. The concepts of "support" of an itemset and correlation of the items within an itemset are related, though not quite the same. This criterion stresses the importance of the actual correlation of the items with one another rather than the absolute support. Previously proposed methods to provide correlated itemsets are not necessarily applicable to very large databases. We provide an algorithm which provides very good computational efficiency, while maintaining statistical robustness. The fact that this algorithm relies on relative measures rather than absolute measures such as support also implies that the method can be applied to find association rules in datasets in which items may appear in a sizeable percentage of the transactions (dense datasets), datasets in which the items have varying density, or even negative association rules.

Copyright © 1998 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 1-3, 1998, Seattle, Washington. ACM Press 1998, ISBN 0-89791-996-3
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References

[1]
Charu C. Aggarwal, Philip S. Yu: Online Generation of Association Rules. ICDE 1998: 402-411 BibTeX
[2]
Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216 BibTeX
[3]
Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499 BibTeX
[4]
Sergey Brin, Rajeev Motwani, Craig Silverstein: Beyond Market Baskets: Generalizing Association Rules to Correlations. SIGMOD Conference 1997: 265-276 BibTeX
[5]
Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur: Dynamic Itemset Counting and Implication Rules for Market Basket Data. SIGMOD Conference 1997: 255-264 BibTeX
[6]
Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996) BibTeX
[7]
Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, A. Inkeri Verkamo: Finding Interesting Rules from Large Sets of Discovered Association Rules. CIKM 1994: 401-407 BibTeX
[8]
Brian Lent, Arun N. Swami, Jennifer Widom: Clustering Association Rules. ICDE 1997: 220-231 BibTeX
[9]
Jong Soo Park, Ming-Syan Chen, Philip S. Yu: Using a Hash-Based Method with Transaction Trimming for Mining Association Rules. IEEE Trans. Knowl. Data Eng. 9(5): 813-825(1997) BibTeX
[10]
...
[11]
Ramakrishnan Srikant, Rakesh Agrawal: Mining Generalized Association Rules. VLDB 1995: 407-419 BibTeX
[12]
Ramakrishnan Srikant, Rakesh Agrawal: Mining Quantitative Association Rules in Large Relational Tables. SIGMOD Conference 1996: 1-12 BibTeX

Referenced by

  1. Ke Wang, Yu He, Jiawei Han: Mining Frequent Itemsets Using Support Constraints. VLDB 2000: 43-52
  2. Shinichi Morishita, Jun Sese: Traversing Itemset Lattice with Statistical Metric Pruning. PODS 2000: 226-236
  3. Philip S. Yu: Data Mining and Personalization Technologies. DASFAA 1999: 6-13
  4. Charu C. Aggarwal, Philip S. Yu: Mining Large Itemsets for Association Rules. IEEE Data Eng. Bull. 21(1): 23-31(1998)
BibTeX
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