Title: Uncharted Forest a Technique for Exploratory Data Analysis,
Abstract: Exploratory data analysis is crucial for developing and understanding
classification models from high-dimensional datasets. We explore the utility of
a new unsupervised tree ensemble called uncharted forest for visualizing class
associations, sample-sample associations, class heterogeneity, and
uninformative classes for provenance studies. The uncharted forest algorithm
can be used to partition data using random selections of variables and metrics
based on statistical spread. After each tree is grown, a tally of the samples
that arrive at every terminal node is maintained. Those tallies are stored in
single sample association matrix and a likelihood measure for each sample being
partitioned with one another can be made. That matrix may be readily viewed as
a heat map, and the probabilities can be quantified via new metrics that
account for class or cluster membership. We display the advantages and
limitations of using this technique by applying it to two classification
datasets and three provenance study datasets. Two of the metrics presented in
this paper are also compared with widely used metrics from two algorithms that
have variance-based clustering mechanisms.