DISCOVERY OF INEXACT CONCEPTS FROM STRUCTURAL DATA

被引：8

作者：

HOLDER, LB

COOK, DJ

机构：

[1] Department of Computer Science Engineering, University of Texas at Arlington, Arlington, TX 76019.

来源：

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 1993年 / 5卷 / 06期

关键词：

CHEMICAL ANALYSIS; DATA COMPRESSION; INEXACT GRAPH MATCH; SCENE ANALYSIS; SUBSTRUCTURE DISCOVERY;

D O I：

10.1109/69.250085

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Concept discovery in structural data requires the identification of repetitive substructures in the data. We describe a method for discovering substructures in data using an inexact graph match. A previous implementation of our SUBDUE system discovers substructures based on the psychologically-motivated criteria of cognitive savings, compactness, connectivity and coverage. However, the instances in the data must exactly match the discovered substructures. We describe a new implementation of SUBDUE that employs an inexact graph match to discover substructures which occur often in the data, but not always in the same form. This inexact substructure discovery can be used to formulate fuzzy concepts, compress the. data description, and discover interesting structures in data that are found either in an identical or in a slightly convoluted form. Examples from the domains of scene analysis and chemical compound analysis demonstrate the benefits of the inexact discovery technique.

引用

页码：992 / 994

页数：3