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Managing email overload with an automatic nonparametric clustering approach

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posted on 06.12.2017, 00:00 by Yang XiangYang Xiang, W Zhou, J Chen
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.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Editor

Li K

Parent Title

Network and parallel computing : IFIP international conference, NPC 2007, Dalian, China, September 18-21, 2007 : proceedings

Start Page

81

End Page

90

Number of Pages

10

ISBN-13

9783540747833

Publisher

Springer

Place of Publication

Berlin, Germany

Open Access

No

Era Eligible

Yes

Number of Chapters

53