@inproceedings{39b94a7925a24f5c8a5aa17293fb3159,
title = "Consensus clustering using partial evidence accumulation",
abstract = "The Evidence Accumulation Clustering, EAC, algorithm is a clustering ensemble method which uses co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. In order to obtain a final consensus clustering the co-association matrix is fed to a pairwise similarity clustering algorithm. The method has thus O(n 2) space complexity, which can constitute a relevant bottleneck to its scalability. In this paper we propose a new formulation which works using a partial set of the co-occurrences, greatly reducing the computational time and space, leading to a scalable algorithm. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.",
keywords = "clustering algorithm, clustering ensembles, evidence accumulation clustering, scalable algorithms",
author = "Andr{\'e} Louren{\c c}o and Bul{\`o}, {Samuel Rota} and Nicola Rebagliati and Ana Fred and M{\'a}rio Figueiredo and Marcello Pelillo",
year = "2013",
doi = "10.1007/978-3-642-38628-2_8",
language = "English",
isbn = "978-3-642-38627-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "69--78",
editor = "Sanches, {Jo{\~a}o M.} and Luisa Mico and Cardoso, {Jaime S.}",
booktitle = "Pattern Recognition and Image Analysis",
address = "Germany",
note = "6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, IbPRIA 2013 ; Conference date: 05-06-2013 Through 07-06-2013",
}