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Fast and effective text mining using linear-time document clustering
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
San Diego, California, United States
Pages: 16 - 22  
Year of Publication: 1999
ISBN:1-58113-143-7
Authors
Bjornar Larsen  SRA International, Inc., 4300 Fair Lakes Court, Fairfax, VA
Chinatsu Aone  SRA International, Inc., 4300 Fair Lakes Court, Fairfax, VA
Sponsors
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 70,   Downloads (12 Months): 511,   Citation Count: 42
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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
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Shohei Kameda , Masayuki Yamamura, Spider algorithm for clustering time series, Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, p.378-383, February 15-17, 2006, Madrid, Spain
 
Tu Bao Ho , Saori Kawasaki , Ngoc Binh Nguyen, Documents clustering using tolerance rough set model and its application to information retrieval, Intelligent exploration of the web, Physica-Verlag GmbH, Heidelberg, Germany, 2003
 
Yukio Uematsu , Ryoji Kataoka , Hiroshi Takeno, Clustering presentation of web image retrieval results using textual information and image features, Proceedings of the 24th IASTED international conference on Internet and multimedia systems and applications, p.217-222, February 13-15, 2006, Innsbruck, Austria
Pu-Jen Cheng , Lee-Feng Chien, Auto-generation of topic hierarchies for web images from users' perspectives, Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA
Florian Beil , Martin Ester , Xiaowei Xu, Frequent term-based text clustering, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada
 
Marina Meilă, Comparing clusterings---an information based distance, Journal of Multivariate Analysis, v.98 n.5, p.873-895, May, 2007
Martin Gavrilov , Dragomir Anguelov , Piotr Indyk , Rajeev Motwani, Mining the stock market (extended abstract): which measure is best?, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.487-496, August 20-23, 2000, Boston, Massachusetts, United States
 
Güneş Erkan, Language model-based document clustering using random walks, Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, p.479-486, June 04-09, 2006, New York, New York
 
Yang Jianwu , Chen Xiaoou, A semi-structured document model for text mining, Journal of Computer Science and Technology, v.17 n.5, p.603-610, May 2002
Hung Chim , Xiaotie Deng, A new suffix tree similarity measure for document clustering, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada
 
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Shui-Lung Chuang , Lee-Feng Chien, A practical web-based approach to generating topic hierarchy for text segments, Proceedings of the thirteenth ACM international conference on Information and knowledge management, November 08-13, 2004, Washington, D.C., USA
 
Rıdvan Saraçoğlu , Kemal Tütüncü , Novruz Allahverdi, A new approach on search for similar documents with multiple categories using fuzzy clustering, Expert Systems with Applications: An International Journal, v.34 n.4, p.2545-2554, May, 2008
Dekang Lin , Patrick Pantel, DIRT @SBT@discovery of inference rules from text, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.323-328, August 26-29, 2001, San Francisco, California
Samer Nassar , Jörg Sander , Corrine Cheng, Incremental and effective data summarization for dynamic hierarchical clustering, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, June 13-18, 2004, Paris, France
Conglei Yao , Yongjian Yu , Sicong Shou , Xiaoming Li, Towards a global schema for web entities, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China
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Illhoi Yoo , Xiaohua Hu, A comprehensive comparison study of document clustering for a biomedical digital library MEDLINE, Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, June 11-15, 2006, Chapel Hill, NC, USA
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Ying Zhao , George Karypis, Evaluation of hierarchical clustering algorithms for document datasets, Proceedings of the eleventh international conference on Information and knowledge management, November 04-09, 2002, McLean, Virginia, USA
Shui-Lung Chuang , Lee-Feng Chien, Taxonomy generation for text segments: A practical web-based approach, ACM Transactions on Information Systems (TOIS), v.23 n.4, p.363-396, October 2005
 
Louis Massey, On the quality of ART1 text clustering, Neural Networks, v.16 n.5-6, p.771-778, June 2003
George Karypis , Eui-Hong (Sam) Han, Fast supervised dimensionality reduction algorithm with applications to document categorization & retrieval, Proceedings of the ninth international conference on Information and knowledge management, p.12-19, November 06-11, 2000, McLean, Virginia, United States
David Cheng , Santosh Vempala , Ravi Kannan , Grant Wang, A divide-and-merge methodology for clustering, Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, June 13-15, 2005, Baltimore, Maryland
 
Dekang Lin , Patrick Pantel, Discovery of inference rules for question-answering, Natural Language Engineering, v.7 n.4, p.343-360, December 2001
 
Yanjun Li , Soon M. Chung , John D. Holt, Text document clustering based on frequent word meaning sequences, Data & Knowledge Engineering, v.64 n.1, p.381-404, January, 2008
 
Chih-Ping Wei , Roger Chiang , Chia-Chen Wu, Accommodating Individual Preferences in the Categorization of Documents: A Personalized Clustering Approach, Journal of Management Information Systems, v.23 n.2, p.173-201, Number 2 / October 2006
 
Reinhold Decker , Soren W. Scholz, Unsupervised Topic Detection in document collections: an application in marketing and business journals, International Journal of Business Intelligence and Data Mining, v.2 n.3, p.347-364, October 2007
 
Maria Halkidi , Benjamin Nguyen , Iraklis Varlamis , Michalis Vazirgiannis, THESUS: Organizing Web document collections based on link semantics, The VLDB Journal — The International Journal on Very Large Data Bases, v.12 n.4, p.320-332, November 2003
David Cheng , Ravi Kannan , Santosh Vempala , Grant Wang, A divide-and-merge methodology for clustering, ACM Transactions on Database Systems (TODS), v.31 n.4, p.1499-1525, December 2006
 
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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION


General Terms:
Design, Documentation, Languages, Management, Measurement, Performance, Theory


Keywords:
clustering, multi-document summarization, text mining

Collaborative Colleagues:
Bjornar Larsen: colleagues
Chinatsu Aone: colleagues

Peer to Peer - Readers of this Article have also read:

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