Exploring the power of heuristics and links in multi-relational data mining

被引：0

作者：

Yin, Xiaoxin ^{[1
]}

Han, Jiawei ^{[2
]}

机构：

[1] Microsoft Res, 1 Microsoft Way, Redmond, WA 98052 USA

[2] Univ Illinois, Urbana, IL USA

来源：

FOUNDATIONS OF INTELLIGENT SYSTEMS, PROCEEDINGS | 2008年 / 4994卷

基金：

美国国家科学基金会;

关键词：

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：

Relational databases are the most popular repository for structured data, and are thus one of the richest sources of knowledge in the world. Because of the complexity of relational data, it is a challenging task to design efficient and scalable data mining approaches in relational databases. In this paper we discuss two methodologies to address this issue. The first methodology is to use heuristics to guide the data mining procedure, in order to avoid aimless, exhaustive search in relational databases. The second methodology is to assign certain property to each object in the database, and let different objects interact with each other along the links. Experiments show that both approaches achieve high efficiency and accuracy in real applications.

引用

页码：17 / +

页数：3

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