KNOWLEDGE DISCOVERY IN MOLECULAR DATABASES

被引：4

作者：

CONKLIN, D ^{[1
]}

FORTIER, S ^{[1
]}

GLASGOW, J ^{[1
]}

机构：

[1] QUEENS UNIV,DEPT CHEM,KINGSTON K7L 3N6,ON,CANADA

来源：

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

关键词：

CASE-BASED REASONING; CHEMICAL INFORMATION RETRIEVAL; CONCEPTUAL CLUSTERING; DESCRIPTION LOGICS; INDEXING; RELATIONAL MODELS; SCENE ANALYSIS; SPATIAL CONCEPTS; SPATIAL REASONING; STRUCTURED CONCEPT FORMATION;

D O I：

10.1109/69.250082

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes an approach to knowledge discovery in complex molecular databases. The machine learning paradigm used is structured concept formation, in which objects described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map.

引用

页码：985 / 987

页数：3

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