Keywords: divisive, monothetic, internal variations
One of the common issues in large dataset analyses is to detect homogeneous groups of objects. We present a divisive hierarchical clustering method for 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 described.