Mining frequent itemsets with tough constraints

被引：0

作者：

Jia, L ^{[1
]}

Pei, RQ ^{[1
]}

Zhang, SQ ^{[1
]}

机构：

[1] Shanghai Univ, Sch Mechatron & Automat, Shanghai, Peoples R China

来源：

2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS | 2002年

关键词：

data mining; association rules; frequent item-sets; tough constraints;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to efficiently sift the useful ones through a large number of mined rules, the constraint-based mining is introduced. Two large classes of constraints--monotone constraints And succinct constraints have been investigated. However, the problem of frequent itemsets mining with tough constraints has not been solved because of the complexity of the constraints. In this paper, we propose two methods which use the order as the pre-process to solve this problem. The first method is to push the tough constraints deeply inside the candidate generation-and-test approach such as Apriori. The second is to combine the constraints with the the pattern-growth methods such as FP-tree.

引用

页码：459 / 462

页数：4