Dynamic clustering of histogram data based on adaptive squared Wasserstein distances

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Histogram data,Partitioning clustering method,Wasserstein distance,Adaptive distance,Symbolic data analysis

论文评审过程：Available online 10 December 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.12.001