Asymptotic evaluation of distance measure on high dimensional vector spaces in text mining

Masayuki Goto, Takashi Ishida, Makoto Suzuki, Shigeichi Hirasawa

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

2 Citations (Scopus)

Abstract

This paper discusses the document classification problems in text mining from the viewpoint of asymptotic statistical analysis. In the problem of text mining, the several heuristics are applied to practical analysis because of its experimental effectiveness in many case studies. The theoretical explanation about the performance of text mining techniques is required and such thinking will give us very clear idea. In this paper, the performances of distance measures used to classify the documents are analyzed from the new viewpoint of asymptotic analysis. We also discuss the asymptotic performance of IDF measure used in the information retrieval field.

Original languageEnglish
Title of host publication2008 International Symposium on Information Theory and its Applications, ISITA2008
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 International Symposium on Information Theory and its Applications, ISITA2008 - Auckland, New Zealand
Duration: 2008 Dec 72008 Dec 10

Publication series

Name2008 International Symposium on Information Theory and its Applications, ISITA2008

Conference

Conference2008 International Symposium on Information Theory and its Applications, ISITA2008
CountryNew Zealand
CityAuckland
Period08/12/708/12/10

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Goto, M., Ishida, T., Suzuki, M., & Hirasawa, S. (2008). Asymptotic evaluation of distance measure on high dimensional vector spaces in text mining. In 2008 International Symposium on Information Theory and its Applications, ISITA2008 [4895453] (2008 International Symposium on Information Theory and its Applications, ISITA2008). https://doi.org/10.1109/ISITA.2008.4895453