Papers Papers/2020



Paper Unlock

Authors: Diogo Silva 1 ; Helena Aidos 2 and Ana Fred 2

Affiliations: 1 Portuguese Air Force Academy, Portugal ; 2 Instituto Superior Técnico, Portugal

ISBN: 978-989-758-173-1

ISSN: 2184-4313

Keyword(s): Clustering Methods, EAC, K-Means, MST, GPGPU, CUDA, Sparse Matrices, Single-Link.

Related Ontology Subjects/Areas/Topics: Clustering ; Ensemble Methods ; Pattern Recognition ; Sparsity ; Theory and Methods

Abstract: The unprecedented collection and storage of data in electronic format has given rise to an interest in automated analysis for generation of knowledge and new insights. Cluster analysis is a good candidate since it makes as few assumptions about the data as possible. A vast body of work on clustering methods exist, yet, typically, no single method is able to respond to the specificities of all kinds of data. Evidence Accumulation Clustering (EAC) is a robust state of the art ensemble algorithm that has shown good results. However, this robustness comes with higher computational cost. Currently, its application is slow or restricted to small datasets. The objective of the present work is to scale EAC, allowing its applicability to big datasets, with technology available at a typical workstation. Three approaches for different parts of EAC are presented: a parallel GPU K-Means implementation, a novel strategy to build a sparse CSR matrix specialized to EAC and Single-Link based on Minimu m Spanning Trees using an external memory sorting algorithm. Combining these approaches, the application of EAC to much larger datasets than before was accomplished. (More)


Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Silva, D.; Aidos, H. and Fred, A. (2016). Efficient Evidence Accumulation Clustering for Large Datasets. In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM, ISBN 978-989-758-173-1; ISSN 2184-4313, pages 367-374. DOI: 10.5220/0005770803670374

author={Diogo Silva. and Helena Aidos. and Ana Fred.},
title={Efficient Evidence Accumulation Clustering for Large Datasets},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM,},


JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - Efficient Evidence Accumulation Clustering for Large Datasets
SN - 978-989-758-173-1
IS - 2184-4313
AU - Silva, D.
AU - Aidos, H.
AU - Fred, A.
PY - 2016
SP - 367
EP - 374
DO - 10.5220/0005770803670374

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.