Iterative denoising

被引：3

作者：

Giles, Kendall E. ^{[1
]}

Trosset, Michael W. ^{[2
]}

Marchette, David J. ^{[3
]}

Priebe, Carey E. ^{[4
]}

机构：

[1] Virginia Commonwealth Univ, Dept Stat Sci & Operat Res, Richmond, VA 23284 USA

[2] Indiana Univ, Dept Stat, Bloomington, IN 47405 USA

[3] USN, Dahlgren Div, Ctr Surface Warfare, Dahlgren, VA 22448 USA

[4] Johns Hopkins Univ, Dept Appl Math & Stat, Baltimore, MD 21218 USA

来源：

COMPUTATIONAL STATISTICS | 2008年 / 23卷 / 04期

关键词：

knowledge discovery; text mining; classification; clustering;

D O I：

10.1007/s00180-007-0090-8

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

One problem in many fields is knowledge discovery in heterogeneous, high-dimensional data. As an example, in text mining an analyst often wishes to identify meaningful, implicit, and previously unknown information in an unstructured corpus. Lack of metadata and the complexities of document space make this task difficult. We describe Iterative Denoising, a methodology for knowledge discovery in large heterogeneous datasets that allows a user to visualize and to discover potentially meaningful relationships and structures. In addition, we demonstrate the features of this methodology in the analysis of a heterogeneous Science News corpus.

引用

页码：497 / 517

页数：21

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