Scalable robust clustering method for large and sparse data
Abstract
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.
Main Authors
Format
Conferences
Conference paper
Published
2018
Subjects
Publication in research information system
Publisher
ESANN
Original source
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdf
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201901281317Käytä tätä linkitykseen.
Parent publication ISBN
978-2-87587-047-6
Review status
Peer reviewed
Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Language
English
Is part of publication
ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Citation
- 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
License
Funder(s)
Academy of Finland
Academy of Finland
Funding program(s)
Profilointi, SA
Akatemiaohjelma, SA
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)
Copyright© Authors, 2018