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Managing email overload with an automatic nonparametric clustering approach

Xiang, Yang, Zhou, Wanlei and Chen, Jinjun 2007, Managing email overload with an automatic nonparametric clustering approach, Lecture notes in computer science, vol. 4672, pp. 81-90.

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Title Managing email overload with an automatic nonparametric clustering approach
Author(s) Xiang, Yang
Zhou, Wanlei
Chen, Jinjun
Journal name Lecture notes in computer science
Volume number 4672
Start page 81
End page 90
Publisher Springer Berlin; IFIP International Federation for Information Processing
Place of publication Berlin, Germany
Publication date 2007
ISSN 0302-9743
1611-3349
Keyword(s) email
overload
text clustering
knowledge management
Summary Email overload is a recent problem that there is increasingly difficulty people have faced to process the large number of emails received daily. Currently this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails and thus this is an effective way to manage email overload problem. However, most current approaches still require significant human input when categorizing emails. In this paper we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. Experiments show our new algorithm outperforms existing text clustering algorithms with higher efficiency in terms of computational time and clustering quality measured by different gauges.
Notes Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by the repository, please contact drosupp@deakin.edu.au
Language eng
Field of Research 080599 Distributed Computing not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, IFIP International Federation for Information Processing; 2008, Springer Berlin
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007834

Document type: Journal Article
Collections: School of Engineering and Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.