Relational peculiarity-oriented mining

被引：11
|
作者：
Ohshima, Muneaki
Zhong, Ning
Yao, Yiyu
Liu, Chunnian
机构：
[1] Maebashi Inst Technol, Dept Informat Engn, Maebashi, Gunma 3710816, Japan
[2] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada
[3] Beijing Univ Technol, Coll Comp Sci, Beijing 100022, Peoples R China
来源：
DATA MINING AND KNOWLEDGE DISCOVERY | 2007年 / 15卷 / 02期
基金：
中国国家自然科学基金;
关键词：
peculiarity-oriented mining; relational data mining; identification of peculiar records; relational peculiarity rules; multi-database mining;
D O I：
10.1007/s10618-006-0046-6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Peculiarity rules are a new type of useful knowledge that can be discovered by searching the relevance among peculiar data. A main task in mining such knowledge is peculiarity identification. Previous methods for finding peculiar data focus on attribute values. By extending to record-level peculiarity, this paper investigates relational peculiarity-oriented mining. Peculiarity rules are mined, and more importantly explained, in a relational mining framework. Several experiments are carried out and the results show that relational peculiarity-oriented mining is effective.
引用
收藏
页码：249 / 273
页数：25
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