Scalable robust clustering method for large and sparse data
Year of publication
2018
Authors
Hämäläinen, Joonas; Kärkkäinen, Tommi; Rossi, Tuomo
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.
Show moreOrganizations and authors
Publication type
Publication format
Article
Parent publication type
Conference
Article type
Other article
Audience
ScientificPeer-reviewed
Peer-ReviewedMINEDU's publication type classification code
A4 Article in conference proceedingsPublication channel information
Parent publication name
Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher
Pages
449-454
ISBN
Publication forum
Publication forum level
1
Open access
Open access in the publisher’s service
No
Self-archived
Yes
Other information
Fields of science
Computer and information sciences
Keywords
[object Object],[object Object]
Publication country
Belgium
Internationality of the publisher
International
Language
English
International co-publication
No
Co-publication with a company
No
The publication is included in the Ministry of Education and Culture’s Publication data collection
Yes