Discovering interesting rules from dense data

被引：0

作者：

Protaziuk, G ^{[1
]}

Soldacki, P ^{[1
]}

Gancarz, L ^{[1
]}

机构：

[1] Warsaw Univ Technol, Inst Comp Sci, PL-00665 Warsaw, Poland

来源：

INTELLIGENT INFORMATION SYSTEMS 2002, PROCEEDINGS | 2002年 / 17卷

关键词：

data mining; association rules; interesting rules; maximal patterns; knowledge exploration;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Discovering association rules is one of the most important tasks in data mining and many efficient algorithms have been proposed in literature. However, the number of discovered rules is often so large, especially in dense data, that the user cannot analyze all discovered rules. To overcome that problem several methods for mining only interesting rules have been proposed. In this paper we describe efficient algorithm for finding maximal, unknown part of association with a given antecedent or consequent in databases with long patterns.

引用

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页码：91 / 100

页数：10

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