NDLI: Text document categorization by term association

Content Provider	IEEE Xplore Digital Library
Author	Antonie, M.-L. Zaiane, O.R.
Copyright Year	2002
Description	Author affiliation: Alberta Univ., Edmonton, Alta., Canada (Antonie, M.-L.; Zaiane, O.R.)
Abstract	A good text classifier is a classifier that efficiently categorizes large sets of text documents in a reasonable time frame and with an acceptable accuracy, and that provides classification rules that are human readable for possible fine-tuning. If the training of the classifier is also quick, this could become in some application domains a good asset for the classifier. Many techniques and algorithms for automatic text categorization have been devised. According to published literature, some are more accurate than others, and some provide more interpretable classification models than others. However, none can combine all the beneficial properties enumerated above. In this paper we present a novel approach for automatic text categorization that borrows from market basket analysis techniques using association rule mining in the data-mining field. We focus on two major problems: (1) finding the best term association rules in a textual database by generating and pruning; and (2) using the rules to build a text classifier. Our text categorization method proves to be efficient and effective, and experiments on well-known collections show that the classifier performs well. In addition, training as well as classification are both fast and the generated rules are human readable.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Pattern Analysis & Machine Intelligence (TCPAMI) IEEE Comput. Soc. Tech. Committee on Computational Intelligence (TCCI)
Starting Page	19
Ending Page	26
File Size	540629
Page Count	8
File Format	PDF
ISBN	0769517544
DOI	10.1109/ICDM.2002.1183881
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-12-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text categorization Data mining Machine learning Humans Association rules Image databases Transaction databases Indexing Information retrieval Electronic mail
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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