An L∞ norm visual classifier

Anushka Anand; Leland Wilkinson; Dang Nhon Tuan

doi:10.1109/ICDM.2009.119

An L^∞ norm visual classifier

Anushka Anand, Leland Wilkinson, Dang Nhon Tuan

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

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 visualprocessing motivated classifier. The accuracy of this classifier on widely-used benchmark datasets rivals the accuracy of competitive classifiers.

Original language	English
Title of host publication	ICDM 2009 - The 9th IEEE International Conference on Data Mining
Pages	687-692
Number of pages	6
DOIs	https://doi.org/10.1109/ICDM.2009.119
State	Published - 2009
Event	9th IEEE International Conference on Data Mining, ICDM 2009 - Miami, FL, United States Duration: Dec 6 2009 → Dec 9 2009

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Conference

Conference	9th IEEE International Conference on Data Mining, ICDM 2009
Country	United States
City	Miami, FL
Period	12/6/09 → 12/9/09

Keywords

Supervised classification
Visual data mining

Access to Document

10.1109/ICDM.2009.119

Link to publication in Scopus

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Cite this

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