NDLI: Clustering Description Extraction Based on Statistical Machine Learning

Content Provider	IEEE Xplore Digital Library
Author	Chengzhi Zhang Hongjiao Xu
Copyright Year	2008
Description	Author affiliation: Inst. of Sci. & Tech. Inf. of China, Beijing (Hongjiao Xu) \|\| Dept. of Inf. Manage., Nanjing Univ. of Sci. & Technol., Nanjing (Chengzhi Zhang)
Abstract	Clustering description problem is one of key issues of the traditional document clustering algorithm. The traditional document algorithm can cluster the objects, but it can not give concept description for the clustered results. Document clustering description is a problem of labeling the clustered results of document collection clustering. It can help users determine whether one of the clusters is relevant to users' information requirement. Therefore, labeling a clustered set of documents is an important and challenging work in document clustering applications. To resolve the problem of the weak readability of the traditional document clustering results, a method of automatic labeling documents clusters based on machine learning is put forward. Experimental results show that the method based on SVM will provide users with more concise and comprehensive document clustering results. It also reflects the linear trend of clustering description problem.
Starting Page	22
Ending Page	26
File Size	357607
Page Count	5
File Format	PDF
ISBN	9780769534978
DOI	10.1109/IITA.2008.114
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Information management Data mining Information technology Support vector machines Learning systems Clustering algorithms Clustering Description Machine learning Frequency Labeling Document Clustering Statistical Machine Learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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
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