An L norm visual classifier

Anushka Anand, Leland Wilkinson, Dang Nhon Tuan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 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 languageEnglish
Title of host publicationICDM 2009 - The 9th IEEE International Conference on Data Mining
Pages687-692
Number of pages6
DOIs
StatePublished - 2009
Event9th IEEE International Conference on Data Mining, ICDM 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 9 2009

Publication series

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

Conference

Conference9th IEEE International Conference on Data Mining, ICDM 2009
Country/TerritoryUnited States
CityMiami, FL
Period12/6/0912/9/09

Keywords

  • Supervised classification
  • Visual data mining

Fingerprint

Dive into the research topics of 'An L<sup>∞</sup> norm visual classifier'. Together they form a unique fingerprint.

Cite this