Information mining: Integrating data mining and text mining for business intelligence

Quanzhi Li, Yi Fang Brook Wu

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

3 Scopus citations

Abstract

Data mining and text mining can help decision makers obtain business intelligence and make informed decisions, but using one of them gives us only a partial picture. The application of data mining can lead to questions that cannot be answered with only numbers. Therefore, decision makers will need text mining to drill the textual data to find explanations for numbers. On the other hand, the application of text mining will also raise questions that cannot be answered with only text. We need to examine and utilize findings from both. However, most of the current text mining applications and data mining applications are not integrated. In this paper, a framework for combining these two technologies is described. In this framework, a taxonomy complemented by feature indexing and full-text indexing will bridge data mining and text mining. The technical challenges of the integration are also discussed.

Original languageEnglish (US)
Title of host publicationAssociation for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006
Pages1400-1406
Number of pages7
StatePublished - 2006
Event12th Americas Conference on Information Systems, AMCIS 2006 - Acapulco, Mexico
Duration: Aug 4 2006Aug 6 2006

Publication series

NameAssociation for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006
Volume3

Other

Other12th Americas Conference on Information Systems, AMCIS 2006
Country/TerritoryMexico
CityAcapulco
Period8/4/068/6/06

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Library and Information Sciences
  • Information Systems

Keywords

  • Business intelligence
  • Data mining
  • Feature indexing
  • Information mining
  • Taxonomy
  • Text mining

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