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Learning with Discrete Least Squares on Multivariate Polynomial Spaces Using Evaluations at Random or Low-Discrepancy Point Sets Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A Parallel Consensus Clustering Algorithm

Authors : Olgierd Unold, Tadeusz Tagowski

Published in: Machine Learning, Optimization, and Big Data

Publisher: Springer International Publishing

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Abstract

Consensus clustering is a stability-based algorithm with a prediction power far better than other internal measures. Unfortunately, this method is reported to be slow in terms of time and hard to scalability. We presented here consensus clustering algorithm optimized for multi-core processors. We showed that it is possible to obtain scalable performance of the compute-intensive algorithm for high-dimensional data such as gene expression microarrays.

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Metadata
Title
A Parallel Consensus Clustering Algorithm
Authors
Olgierd Unold
Tadeusz Tagowski
Copyright Year
2015
Book
Machine Learning, Optimization, and Big Data

Print ISBN: 978-3-319-27925-1

Electronic ISBN: 978-3-319-27926-8

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-27926-8_28

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