Efficient mining of association rules using closed itemset lattices

被引：470

作者：

Pasquier, N ^{[1
]}

Bastide, Y ^{[1
]}

Taouil, R ^{[1
]}

Lakhal, L ^{[1
]}

机构：

[1] Univ Clermont Ferrand 2, Lab Informat, LIMOS, F-63177 Clermont Ferrand, France

来源：

INFORMATION SYSTEMS | 1999年 / 24卷 / 01期

关键词：

data mining; knowledge discovery; association rules; data clustering; lattices; algorithms;

D O I：

10.1016/S0306-4379(99)00003-4

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIG, that are all based on the Apriori mining:method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to-an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data. (C)1999 Elsevier Science Ltd. All rights reserved.

引用

页码：25 / 46

页数：22

共 21 条

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