A scale-independent, noise-resistant dissimilarity for tree-based clustering of mixed data
Buttrey, Samuel E.
Whitaker, Lyn R.
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Clustering techniques divide observations into groups.Current techniques usually rely on measurements of dissimilarities between pairs of observations, between pairs of clusters, and between an observation and a cluster.For numeric variables, these dissimilarity measurements often depend on the scaling of the variables, are changed by monotonic transformations, and do not provide for selection of “important" variables.In our scheme, we fit a set of regression or classification trees with each variable acting in turn as the “response" variable.Points are “close" to one another if they tend to appear in the same leaves of these trees.Trees with poor predictive power are discarded.Therefore, “noise" variables will often appear in none of the trees and have no effect on the clustering. Because our technique uses trees, the dissimilarities are unaffected by linear transformations of the numeric variables and resistant to monotonic ones and to outliers.Categorical variables are included automatically and missing values handled in a natural way.We demonstrate the performance of this technique by using these dissimilarities to cluster some well-known data sets to which noise has been added.
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NPS Report NumberNPS-OR-16-003
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Shaham, Yoav (Monterey, California: Naval Postgraduate School, 2015-09);This research explores the use of the tree distances of Buttrey and Whitaker to visualize multidimensional data of mixed-variable types, having both numerical and categorical data. Tree distances measure dissimilarities ...
Lee, Suyoung (Monterey, California: Naval Postgraduate School, 2016-03);Modern data sets often consist of unstructured data and mixed data; that is, they include both numerical and categorical variables. Often, these data sets will include noise, redundancy, missing values and outliers. ...
Buttrey, Samuel E.; Whitaker, Lyn R. (2015);This paper describes treeClust, an R package that produces dissimilarities useful for clustering. These dissimilarities arise from a set of classification or regression trees, one with each variable in the data acting ...