Feature Diversity in Cluster Ensembles for Robust Document Clustering
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Author
Other authors
Publication date
2006-08Abstract
The performance of document clustering systems depends
on employing optimal text representations, which are not
only difficult to determine beforehand, but also may vary
from one clustering problem to another. As a first step towards building robust document clusterers, a strategy based
on feature diversity and cluster ensembles is presented in this
work. Experiments conducted on a binary clustering problem show that our method is robust to near-optimal model
order selection and able to detect constructive interactions
between different document representations in the test bed.
Document Type
Object of conference
Language
English
Subject (CDU)
004 - Computer science and technology. Computing. Data processing
62 - Engineering. Technology in general
Keywords
Intel·ligència artificial -- Aplicacions a l'enginyeria
Algorismes
Pages
2 p.
Publisher
29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, 6-11 of August 2006
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Rights
© Association for Computing Machinery. Tots els drets reservats