Blind method for discovering number of clusters in multidimensional datasets by regression on linkage hierarchies generated from random data.

ABSTRACT: Determining intrinsic number of clusters in a multidimensional dataset is a commonly encountered problem in exploratory data analysis. Unsupervised clustering algorithms often rely on specification of cluster number as an input parameter. However, this is typically not known a priori. Many methods have been proposed to estimate cluster number, including statistical and information-theoretic approaches such as the gap statistic, but these methods are not always reliable when applied to non-normally distributed datasets containing outliers or noise. In this study, I propose a novel method called hierarchical linkage regression, which uses regression to estimate the intrinsic number of clusters in a multidimensional dataset. The method operates on the hypothesis that the organization of data

SUBMITTER: Zalay OC 

PROVIDER: S-EPMC6977736 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature