DMCA
Dynamic Itemset Counting and Implication Rules for Market Basket Data (1997)
Cached
Download Links
- [www.cs.ucla.edu]
- [sage.chungbuk.ac.kr]
- CiteULike
- DBLP
Other Repositories/Bibliography
| Citations: | 594 - 6 self |
BibTeX
@INPROCEEDINGS{Brin97dynamicitemset,
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 = {},
year = {1997},
pages = {255--264},
publisher = {ACM Press}
}
Share
 |
 |
 |
 |
||
OpenURL
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 the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed to synthetic data, can dramatically affect the performance of the system and the form of the results. 1 Introduction Within the area of data mining, the problem of deriving associations from data has recently received a great deal of attention. The prob...
Keyphrases
implication rule dynamic itemset counting market basket data new algorithm synthetic data great deal data mining real data truly implication candidate itemsets market-basket data present several important contribution different characteristic item reordering low-level efficiency intuitive result large itemsets new way classic algorithm
