research-article

Document classification based on web search hit counts

Authors:

Masaya Kaneko,

Shusuke Okamoto,

Masaki Kohana,

You InayoshiAuthors Info & Claims

IIWAS '12: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services

Pages 223 - 228

https://doi.org/10.1145/2428736.2428772

Published: 03 December 2012 Publication History

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Abstract

This paper describes a web mining method to classify research documents automatically. Web hit counts of AND-search on two words are used to form a document vector. Target documents are classified with a result of k-means clustering method, in which cosine similarity is used to calculate a distance.

References

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Cited By

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Kohana MOkamoto SKaneko M(2013)A Clustering Algorithm Using Twitter User BiographyProceedings of the 2013 16th International Conference on Network-Based Information Systems10.1109/NBiS.2013.70(432-435)Online publication date: 4-Sep-2013
https://dl.acm.org/doi/10.1109/NBiS.2013.70
Yadav SSain D(2013)An efficient technique for finding semantic similarity and their frequency between words2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE)10.1109/ICGCE.2013.6823420(159-163)Online publication date: Dec-2013
https://doi.org/10.1109/ICGCE.2013.6823420

Index Terms

Document classification based on web search hit counts
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

IIWAS '12: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services

December 2012

432 pages

ISBN:9781450313063

DOI:10.1145/2428736

General Chair:
Eric Pardede
La Trobe University, Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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ACM: Association for Computing Machinery

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2012

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IIWAS '12

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@WAS

IIWAS '12: The 14th International Conference on Information Integration and Web-based Applications & Services

December 3 - 5, 2012

Bali, Indonesia

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Kohana MOkamoto SKaneko M(2013)A Clustering Algorithm Using Twitter User BiographyProceedings of the 2013 16th International Conference on Network-Based Information Systems10.1109/NBiS.2013.70(432-435)Online publication date: 4-Sep-2013
https://dl.acm.org/doi/10.1109/NBiS.2013.70
Yadav SSain D(2013)An efficient technique for finding semantic similarity and their frequency between words2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE)10.1109/ICGCE.2013.6823420(159-163)Online publication date: Dec-2013
https://doi.org/10.1109/ICGCE.2013.6823420

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A semantic weighting method for document classification based on Markov logic networks