DMCA
CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets (2000)
| Citations: | 312 - 28 self |
BibTeX
@INPROCEEDINGS{Pei00closet:an,
author = {Jian Pei and Jiawei Han and Runying Mao},
title = {CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets},
booktitle = {},
year = {2000},
pages = {21--30}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an efficient algorithm, CLOSET, for mining closed itemsets, with the development of three techniques: (1) applying a compressed, frequent pattern tree FP-tree structure for mining closed itemsets without candidate generation, (2) developing a single prefix path compression technique to identify frequent closed itemsets quickly, and (3) exploring a partition-based projection mechanism for scalable mining in large databases. Our performance study shows that CLOSET is efficient and scalable over large databases, and is faster than the previously proposed methods. 1 Introduction It has been well recognized that frequent pattern minin...
Keyphrases
efficient algorithm mining frequent association mining large database recent study performance study single prefix path compression technique scalable mining large set frequent itemsets frequent pattern tree fp-tree structure association rule frequent pattern minin candidate generation partition-based projection mechanism interesting alternative corresponding rule
