Maximizing the predictive value of production rules
Artificial Intelligence
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Multidimensional binary search trees used for associative searching
Communications of the ACM
H-BLOB: a hierarchical visual clustering method using implicit surfaces
Proceedings of the conference on Visualization '00
Mixtures of Rectangles: Interpretable Soft Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Simple and effective visual models for gene expression cancer diagnostics
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
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Many classification algorithms suffer from a lack of human interpretability. Using such classifiers to solve real world problems often requires blind faith in the given model. In this paper we present a novel approach to classification that takes into account interpretability and visualization of the results. We attempt to efficiently discover the most relevant snapshot of the data, in the form of a two-dimensional scatter plot with easily understandable axes. We then use this plot as the basis for a classification algorithm. Furthermore, we investigate the trade-off between classification accuracy and interpretability by comparing the performance of our classifier on real data with that of several traditional classifiers. Upon evaluating our algorithm on a wide range of canonical data sets we find that, in most cases, it is possible to obtain additional interpretability with little or no loss in classification accuracy.