The scalability of learning algorithms has always been a central concern for data mining
researchers, and nowadays, with the rapid increase in data storage capacities and
availability, its importance has increased. To this end, sampling has been studied by several
researchers in an effort to derive sufficiently accurate models using only small data fractions.
In this article we focus on spectral k-means, that is, the k-means approximation as derived by
the spectral relaxation, and propose a sequential sampling framework that iteratively …

A sequential sampling framework for spectral κ-means based on efficient bootstrap
accuracy estimations: Application to distributed clustering … A sequential sampling
framework for spectral κ-means based on efficient bootstrap accuracy estimations:
Application to distributed clustering …