 |
|
|
A New Framework For Itemset Generation |
Full Paper (PDF)
Slides (PDF)
|
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. |
References, where available, link to the DBLP on the World Wide Web.
[1]Charu C. Aggarwal, Philip S. Yu:
Online Generation of Association Rules.
ICDE 1998: 402-411[2]Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami:
Mining Association Rules between Sets of Items in Large Databases.
SIGMOD Conference 1993: 207-216[3]Rakesh Agrawal, Ramakrishnan Srikant:
Fast Algorithms for Mining Association Rules in Large Databases.
VLDB 1994: 487-499[4]Sergey Brin, Rajeev Motwani, Craig Silverstein:
Beyond Market Baskets: Generalizing Association Rules to Correlations.
SIGMOD Conference 1997: 265-276[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[6]Ming-Syan Chen, Jiawei Han, Philip S. Yu:
Data Mining: An Overview from a Database Perspective.
TKDE 8(6): 866-883(1996)[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[8]Brian Lent, Arun N. Swami, Jennifer Widom:
Clustering Association Rules.
ICDE 1997: 220-231[9]Jong Soo Park, Ming-Syan Chen, Philip S. Yu:
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules.
TKDE 9(5): 813-825(1997)[10]...
[11]Ramakrishnan Srikant, Rakesh Agrawal:
Mining Generalized Association Rules.
VLDB 1995: 407-419[12]Ramakrishnan Srikant, Rakesh Agrawal:
Mining Quantitative Association Rules in Large Relational Tables.
SIGMOD Conf. 1996: 1-12
Referenced By:
- Charu C. Aggarwal, Philip S. Yu:
Mining Large Itemsets for Association Rules.
Data Engineering Bulletin 21(1): 23-31(1998)
|
@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 = {0-89791-966-3},
pages = {18-24},
crossref = {DBLP:conf/pods/98},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
|
DBLP: Copyright ©1999 by Michael Ley (ley@uni-trier.de).
|
|
