Document clustering using character N-grams: A comparative evaluation with term-based and word-based clustering

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2005, Pages 357-358

  Miao, Yingbo ^a   Kešelj, Vlado ^a   Milios, Evangelos ^a  

^a DALHOUSIE UNIVERSITY   (Canada)

Author keywords

N gram text representation; Text clustering; Text mining

Indexed keywords

N-GRAM TEXT REPRESENTATION; TEXT CLUSTERING; TEXT MINING; WORD-BASED CLUSTERING;

ALGORITHMIC LANGUAGES; CHARACTER SETS; DATA ACQUISITION; QUALITY CONTROL; ROBUSTNESS (CONTROL SYSTEMS); VECTORS;

WORD PROCESSING;

EID: 33745771177     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1099554.1099665     Document Type: Conference Paper

References (5)

1. W. B. Cavnar. Using an n-gram-based document representation with a vector processing retrieval model. In TREC-3, pages 269-278, 1994.
2. K. Frantzi, S. Ananiadou, and H. Mima. Automatic recognition of multi-word terms: the C-value/NC-value method. International Journal on Digital Libraries, 3(2):115-130, August 2000.
3. V. Kešelj, F. Peng, N. Cercone, and C. Thomas. N-gram-based author profiles for authorship attribution. In PACLING'03, pages 255-264, August 2003.
4. J. Kogan, C. Nicholas, and V. Volkovich. Text mining with information-theoretical clustering. Computing in Science and Engineering, May 2003 (accepted).
5. Y. Miao, V. Kešelj, and E. Milios. Document clustering using character n-grams: A comparative evaluation with term-based and word-based clustering. Technical report, http://www.cs.dal.ca/research/techreports/2005/, Faculty of Computer Science, Dalhousie University, 2005.