Scalable robust clustering method for large and sparse data
Joonas Hämäläinen, Tommi Kärkkäinen, Tuomo Rossi · Jyväskylä University Digital Archive (University of Jyväskylä) · 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.