NDLI: Document Representation and Dimension Reduction for Text Clustering

Content Provider	IEEE Xplore Digital Library
Author	Shafiei, M. Singer Wang Zhang, R. Milios, E. Bin Tang Tougas, J. Spiteri, R.
Copyright Year	2007
Description	Author affiliation: Dalhousie Univ., Halifax (Shafiei, M.; Singer Wang; Zhang, R.; Milios, E.; Bin Tang; Tougas, J.; Spiteri, R.)
Abstract	Increasingly large text damsels and the high dimensionality associated with natural language create a great challenge in text mining, In this research, a systematic study is conducted. in which three different document representation methods for text are used, together with three Dimension Reduction Techniques (DRT), in the context of the text clustering problem. Several standard benchmark datasets are used. The three Document representation methods considered are based on the vector space model, and they include word, multi-word term, and character N-gram representations. The dimension reduction methods are. independent component analysis (ICA). latent semantic indexing (LSI), and a feature selection technique based on Document Frequency (DF). Results are compared in terms of clustering performance, using the k-means clustering algorithm. Experiments show that ICA and LSI are clearly belter than DF on all darascls. For word and N-gram representation. ICA generally gives better results compared with LSI. Experiments also show that the word representation gives better clustering results compared to term and N-gram representation. Finally, for the N-gram representation, it is demonstrated that a profile length (before dimensionality reduction) of 2000 is sufficient to capture the information and in most cases, a -4-gram representation gives better performance than 3-gram representation.
Starting Page	770
Ending Page	779
File Size	730699
Page Count	10
File Format	PDF
ISBN	9781424408313
DOI	10.1109/ICDEW.2007.4401066
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-17
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Text mining Natural languages Clustering algorithms Independent component analysis Frequency Large scale integration Communications technology Data mining Indexing
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

Latent Dirichlet Co-Clustering

Document representation and dimension reduction for text clustering (2007).

A Feature Reduction Technique for Improved Web Page Clustering

RPCL text clustering based on concept indexing

Comparing Dimension Reduction Techniques for Document Clustering

A systematic study on document representation and dimensionality reduction for text clustering a systematic study on document representation and dimensionality reduction for text clustering.

Text Document Preprocessing and Dimension Reduction Techniques for Text Document Clustering

Decomposition of term-document matrix representation for clustering analysis

High performance in minimizing of term-document matrix representation for document clustering

Document Representation and Dimension Reduction for Text Clustering

Similar Documents

Latent Dirichlet Co-Clustering

Document representation and dimension reduction for text clustering (2007).

A Feature Reduction Technique for Improved Web Page Clustering

RPCL text clustering based on concept indexing

Comparing Dimension Reduction Techniques for Document Clustering

A systematic study on document representation and dimensionality reduction for text clustering a systematic study on document representation and dimensionality reduction for text clustering.

Text Document Preprocessing and Dimension Reduction Techniques for Text Document Clustering

Decomposition of term-document matrix representation for clustering analysis

High performance in minimizing of term-document matrix representation for document clustering

Document Representation and Dimension Reduction for Text Clustering