Scalable robust clustering method for large and sparse data
Hämäläinen, J., Kärkkäinen, T., & Rossi, T. (2018). Scalable robust clustering method for large and sparse data. In ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 449-454). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdf
Date
2018Copyright
© Authors, 2018
Datasets for unsupervised clustering can be large and sparse, with significant portion of missing values. We present here a scalable version of a robust clustering method with the available data strategy. Moreprecisely, a general algorithm is described and the accuracy and scalability of a distributed implementation of the algorithm is tested. The obtained results allow us to conclude the viability of the proposed approach.
Publisher
ESANNParent publication ISBN
978-2-87587-047-6Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningIs part of publication
ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningKeywords
Original source
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/28889218
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Academy of FinlandFunding program(s)
Research profiles, AoF; Academy Programme, AoF
Additional information about funding
The work of TK has been supported by the Academy of Finland from the projects 311877 (Demo) and 315550 (HNP-AI)License
Except where otherwise noted, this item's license is described as In Copyright