A density-sensitive hierarchical clustering method

2012/10/23 by Martínez-Pérez, Álvaro
#62H30 #68T10 #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.1210.6292

Abstract

We define a hierarchical clustering method: α-unchaining single linkage or SL(α). The input of this algorithm is a finite metric space and a certain parameter α. This method is sensitive to the density of the distribution and offers some solution to the so called chaining effect. We also define a modified version, SL^*(α), to treat the chaining through points or small blocks. We study the theoretical properties of these methods and offer some theoretical background for the treatment of chaining effects.

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