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Hierarchical clustering for histogram data

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Clustering methods for classical data are well established, though the associated algorithms primarily focus on partitioning methods and agglomerative hierarchical methods. With the advent of massively large data sets, too large to be analyzed by traditional techniques, new paradigms are needed. Symbolic data methods form one solution to this problem. While symbolic data can be important and arise naturally in their own right, they are particularly relevant when faced with data that emerged from aggregation of (larger) data sets. One format is when the data are histogram‐valued in ℝp, instead of points in ℝp as in classical data. This paper looks at the problem of constructing hierarchies using a divisive polythetic algorithm based on dissimilarity measures derived for histogram observations. WIREs Comput Stat 2017, 9:e1405. doi: 10.1002/wics.1405 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification
Simulated data: all S = 5 data sets.
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Validity indices for diabetes data.
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Hierarchy tree for diabetes data: (a) CDF, (b) Euclidean Ichino–Yaguchi, (c) extended Gowda–Diday and (d) extended de Carvalho.
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Hierarchy tree for simulated data: (a) CDF and (b) Euclidean Ichino–Yaguchi.
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Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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