ABSTRACT-DRIVEN PATTERN DISCOVERY IN DATABASES

被引：28
|
作者：
DHAR, V
TUZHILIN, A
机构：
[1] Information Systems Department, New York University, Stern School of Business
来源：
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 1993年 / 5卷 / 06期
关键词：
CLASSIFICATION; DATA ABSTRACTION; GENERALIZATION; PATTERN DISCOVERY;
D O I：
10.1109/69.250075
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we study the problem of discovering interesting patterns in large volumes of data. Patterns can be expressed not only in terms of the database schema but also in user-defined terms, such as relational views and classification hierarchies. The user-defined terminology is stored in a data dictionary that maps it into the language of the database schema. We define a pattern as a deductive rule expressed in user-defined terms that has a degree of certainty associated with it. We present methods of discovering interesting patterns based on abstracts which are summaries of the data expressed in the language of the user.
引用
收藏
页码：926 / 938
页数：13
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