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

Kärkkäinen, Tommi
Rossi, Tuomo
Hämäläinen, Joonas

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

Fields of science: