Double monothetic clustering for histogram-valued data
Jaejik Kim, Lynne Billard · Communications for Statistical Applications and Methods · 2018
One of the common issues in large dataset analyses is to detect and construct homogeneous groups of objects in those datasets.This is typically done by some form of clustering technique.In this study, we present a divisive hierarchical clustering method for two monothetic characteristics of histogram data.Unlike classical data points, a histogram has internal variation of itself as well as location information.However, to find the optimal bipartition, existing divisive monothetic clustering methods for histogram data consider only location information as a monothetic characteristic and they cannot distinguish histograms with the same location but different internal variations.Thus, a divisive clustering method considering both location and internal variation of histograms is proposed in this study.The method has an advantage in interpreting clustering outcomes by providing binary questions for each split.The proposed clustering method is verified through a simulation study and applied to a large U.S. house property value dataset.