@inproceedings{3c37a082e2f04d06b0502b0cd37f5342,
title = "Feature diversity in cluster ensembles for robust document clustering",
abstract = "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.",
keywords = "Cluster ensembles, Document clustering, Feature extraction",
author = "Xavier Sevillano and Germ{\'a}n Cobo and Francesc Al{\'i}as and Socor{\'o}, {Joan Claudi}",
year = "2006",
doi = "10.1145/1148170.1148323",
language = "English",
isbn = "1595933697",
series = "Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery (ACM)",
pages = "697--698",
booktitle = "Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",
address = "United States",
note = "29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ; Conference date: 06-08-2006 Through 11-08-2006",
}