Stochastic estimations of a total number of classes for the clusterings with too enormous samples to be accommodate into a clustering engine

Keishu Utimula, Genki I. Prayogo, Kousuke Nakano +2

We considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accomm…