Dynamic clustering of histogram data based on adaptive squared Wasserstein distances
Histogram-valued data are treating differently from bar-count data. A new clustering method for histogram-valued data is proposed. Two adaptive clustering strategy are proposed. A set of quality-of-partition indices are proposed. No other clustering method exist for histogram-valued data.
histogram data wasserstein distance partitioning clustering method adaptive distance symbolic data analysis