Managing email overload with an automatic nonparametric clustering system

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Abstract

Email overload is a recent problem that there is increasingly difficulty that people have 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 the email overload problem. However, most current approaches still require significant human input for 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. The evaluation shows our new algorithm outperforms existing text clustering algorithms with higher efficiency and quality in terms of computational time and clustering quality measured by different gauges. The experimental results also well match the labeled human clustering results.

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