An L-infinity Norm Visual Classifier

Authors:
Anushka Anand;Leland Wilkinson;Dang Nhon Tuan
Affiliations:
-;-;-
Venue:
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Year:
2009

Citing 0
Cited 2

CHIRP: a new classifier based on composite hypercubes on iterated random projections

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Substantial improvements in the set-covering projection classifier CHIRP (composite hypercubes on iterated random projections)

ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011

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Abstract

We introduce a mathematical framework, based on the L-infinity norm distance metric, to describe human interactions in a visual data mining environment. We use the framework to build a classifier that involves an algebra on hyper-rectangles. Our classifier, called VisClassifier, generates set-wise rules from simple gestures in an exploratory visual GUI. Logging these rules allows us to apply our analysis to a new sample or batch of data so that we can assess the predictive power of our visual-processing motivated classifier. The accuracy of this classifier on widely-used benchmark datasets rivals the accuracy of competitive classifiers.