Towards a text mining methodology using association rule extraction

被引：11

作者：

Cherfi, H ^{[1
]}

Napoli, A ^{[1
]}

Toussaint, Y ^{[1
]}

机构：

[1] Nancy Univ, INRIA, CNRS, LORIA, F-54506 Vandoeuvre Les Nancy, France

来源：

SOFT COMPUTING | 2006年 / 10卷 / 05期

关键词：

Molecular Biology; Mathematical Logic; Quality Measure; Association Rule; Knowledge Discovery;

D O I：

10.1007/s00500-005-0504-x

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 proposes a methodology for text mining relying on the classical knowledge discovery loop, with a number of adaptations. First, texts are indexed and prepared to be processed by frequent itemset levelwise search. Association rules are then extracted and interpreted, with respect to a set of quality measures and domain knowledge, under the control of an analyst. The article includes an experimentation on a real-world text corpus holding on molecular biology.

引用

页码：431 / 441

页数：11

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