Local Induction of Decision Trees: Towards Interactive Data Mining

Truxton Fulton, Simon Kasif, Steven Salzberg, David Waltzt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Decision trees are an important data mining tool with many applications. Like many classification techniques, decision trees process the entire data base in order to produce a generalization of the data that can be used subsequently for classification. Large, complex data bases are not always amenable to such a global approach to generalization. This paper explores several methods for extracting data that is local to a query point, and then using the local data to build generalizations. These adaptively constructed neighborhoods can provide additional information about the query point. Three new algorithms are presented, and experiments using these algorithms are described.

Original languageEnglish (US)
Title of host publicationProceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996
EditorsEvangelos Simoudis, Jiawei Han, Usama M. Fayyad
PublisherAAAI Press
Pages14-19
Number of pages6
ISBN (Electronic)1577350049, 9781577350040
StatePublished - 1996
Externally publishedYes
Event2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996 - Portland, United States
Duration: Aug 2 1996Aug 4 1996

Publication series

NameProceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

Conference

Conference2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996
Country/TerritoryUnited States
CityPortland
Period8/2/968/4/96

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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