@inproceedings{c87388b466c442a1abc2cd430069d0d2,
title = "Refining pairwise similarity matrix for cluster ensemble problem with cluster relations",
abstract = "Cluster ensemble methods have recently emerged as powerful techniques, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. This paper presents two new similarity matrices, which are empirically evaluated and compared against the standard co-association matrix on six datasets (both artificial and real data) using four different combination methods and six clustering validity criteria. In all cases, the results suggest the new link-based similarity matrices are able to extract efficiently the information embedded in the input clusterings, and regularly suggest higher clustering quality in comparison to their competitor.",
keywords = "Cluster ensembles, Cluster relation, Link analysis, Pairwise similarity matrix",
author = "Natthakan Iam-On and Tossapon Boongoen and Simon Garrett",
year = "2008",
doi = "10.1007/978-3-540-88411-8_22",
language = "English",
isbn = "3540884106",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "222--233",
editor = "Jean-Francois Boulicaut and Berthold, {Michael R.} and {Horv{\'a}th }, {Tam{\'a}s }",
booktitle = "Discovery Science - 11th International Conference, DS 2008, Proceedings",
address = "Switzerland",
note = "11th International Conference on Discovery Science, DS 2008 ; Conference date: 13-10-2008 Through 16-10-2008",
}