An Automatic Email Management Approach Using Data Mining Techniques

Abstract

Email mining provides solution to email overload problem by automatically placing emails into some meaningful and similar groups based on email subject and contents. Existing email mining systems such as BuzzTrack, do not consider the semantic similarity between email contents, and when large number of email messages are clustered to a single folder it retains the problem of email overload. The goal of this paper is to solve the problem of email overload through semantically structuring the user’s email by automatically organizing email in folders and sub-folders using data mining clustering technique and extracting important terms from created folders using Apriori-based method for folder identification. This paper proposes a system named AEMS for automatic folder and sub-folder creation and later indexing the created folders. For AEMS module, a novel approach named Semantic non-parametric K-Means++ clustering is proposed for folder creation. Experiments show the effectiveness and efficiency of the proposed techniques using large volumes of email datasets.