Title :
An L-infinity Norm Visual Classifier
Author :
Anand, A. ; Wilkinson, Leland ; Tuan, Dang Nhon
Author_Institution :
Univ. of Illinois at Chicago, Chicago, IL, USA
Abstract :
We introduce a mathematical framework, based on the L∞ 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.
Keywords :
data mining; graphical user interfaces; pattern classification; L∞ norm distance metric; L∞ norm visual classifier; VisClassifier; exploratory visual GUI; human interaction; mathematical framework; set wise rules generation; visual data mining environment; visual processing motivated classifier; Algebra; Classification algorithms; Clustering algorithms; Computer errors; Data mining; Graphical user interfaces; Humans; Machine learning; Partitioning algorithms; Pattern recognition; Visual data mining; supervised classification;
Conference_Titel :
Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-5242-2
Electronic_ISBN :
1550-4786
DOI :
10.1109/ICDM.2009.119