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
Päivämäärä
2018Tekijänoikeudet
© 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.
Julkaisija
ESANNEmojulkaisun ISBN
978-2-87587-047-6Konferenssi
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningKuuluu julkaisuun
ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningAsiasanat
Alkuperäislähde
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdfJulkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/28889218
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Profilointi, SA; Akatemiaohjelma, SALisätietoja rahoituksesta
The work of TK has been supported by the Academy of Finland from the projects 311877 (Demo) and 315550 (HNP-AI)Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
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