Consensus clustering using partial evidence accumulation

André Lourenço, Samuel Rota Bulò, Nicola Rebagliati, Ana Fred, Mário Figueiredo, Marcello Pelillo

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis
Subtitle of host publication6th Iberian Conference IbPRIA 2013
EditorsJoão M. Sanches, Luisa Mico, Jaime S. Cardoso
Place of PublicationBerlin - Heidelberg
ISBN (Electronic)978-3-642-38628-2
ISBN (Print)978-3-642-38627-5
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
Event6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013 - Funchal, Madeira, Portugal
Duration: 5 Jun 20137 Jun 2013

Publication series

SeriesLecture Notes in Computer Science


Conference6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013
Abbreviated titleIbPRIA 2013
CityFunchal, Madeira


  • clustering algorithm
  • clustering ensembles
  • evidence accumulation clustering
  • scalable algorithms


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