Online Program

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Friday, May 18
Data Science
Statistical Analytics for Data Science
Fri, May 18, 1:30 PM - 3:00 PM
Grand Ballroom G

Clustering Histogram-valued Data (304336)

*Lynne Billard, University of Georgia 
Jaejik Kim, Sungkyunkwan University 

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.