@inproceedings{b46a4ef81a994d8c800e601c47a98179,
title = "A hierarchical consensus architecture for robust document clustering",
abstract = "A major problem encountered by text clustering practitioners is the difficulty of determining a priori which is the optimal text representation and clustering technique for a given clustering problem. As a step towards building robust document partitioning systems, we present a strategy based on a hierarchical consensus clustering architecture that operates on a wide diversity of document representations and partitions. The conducted experiments show that the proposed method is capable of yielding a consensus clustering that is comparable to the best individual clustering available even in the presence of a large number of poor individual labelings, outperforming classic non-hierarchical consensus approaches in terms of performance and computational cost.",
author = "Xavier Sevillano and Germ{\'a}n Cobo and Francesc Al{\'i}as and Socor{\'o}, {Joan Claudi}",
year = "2007",
doi = "10.1007/978-3-540-71496-5_82",
language = "English",
isbn = "3540714944",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "741--744",
booktitle = "Advances in Information Retrieval - 29th European Conference on IR Research, ECIR 2007, Proceedings",
address = "Germany",
note = "29th European Conference on IR Research, ECIR 2007 ; Conference date: 02-04-2007 Through 05-04-2007",
}