A4 Article in conference proceedings
Scalable robust clustering method for large and sparse data (2018)
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
JYU authors or editors
Publication details
All authors or editors: Hämäläinen, Joonas; Kärkkäinen, Tommi; Rossi, Tuomo
Parent publication: ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Conference:
- European symposium on artificial neural networks, computational intelligence and machine learning
ISBN: 978-2-87587-047-6
Publication year: 2018
Pages range: 449-454
Publisher: ESANN
Publication country: Belgium
Publication language: English
Persistent website address: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdf
Publication open access: Other way freely accessible online
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/62747
Additional information: ESANN 2018 : 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 25-26-27, 2018.
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.
Keywords: data; cluster analysis
Free keywords: datasets; clustering
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Research Council of Finland
- STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE
- Kärkkäinen, Tommi
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2018
JUFO rating: 1