Efficient incremental phrase-based document clustering

Ahmad M. Bakr, Noha Yousri, Mohamed A. Ismail

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

1 Citation (Scopus)

Abstract

Document clustering has become inevitable for applications that aim to extract information from huge corpuses. Such applications face two main challenges; one is the efficient representation of the documents, along with using an efficient similarity measure, and the second is dealing with the dynamic nature of the corpus. In this paper, an efficient document clustering model is introduced for incrementally storing and updating clusters of a dataset. A new phrase-based similarity method is developed along with the model to calculate the similarity between documents and clusters. Experimental results show that the new clustering model can achieve more accurate results than the traditional algorithms.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages517-520
Number of pages4
Publication statusPublished - 1 Dec 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 11 Nov 201215 Nov 2012

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1215/11/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Bakr, A. M., Yousri, N., & Ismail, M. A. (2012). Efficient incremental phrase-based document clustering. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 517-520). [6460185]