Uncharted Forest a Technique for Exploratory Data Analysis of Provenance Studies
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Exploratory data analysis is a crucial task for developing effective classification models from high dimensional datasets. We explore the utility of a new unsupervised tree ensemble which we call, uncharted forest, for purposes of elucidating class associations, sample-sample associations, class heterogeneity, and uninformative classes for provenance studies. Uncharted forest partitions data along random variables which offer the most gain from various gain metrics, namely variance. After each tree is grown, a tally of every terminal node's sample membership is constructed such that a probabilistic measure for each sample being partitioned with one another can be stored in one matrix. That matrix may be readily viewed as a heat map, and the probabilities can be quantified via metrics which account for class or cluster membership. We display the advantages and limitations of this technique by applying it to 1 exemplary dataset and 3 provenance study datasets. The method is also validated by comparing the sample association metrics to clustering algorithms with known variance based clustering mechanisms.
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