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A proper folder recommendation technique using frequent itemsets for efficient e-mail classification

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2011, 16(2), pp.33-46
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

문종필 1 이원석 2 장중혁 3

1KT 이노츠 연구개발본부
2연세대학교
3대구대학교

Accredited

ABSTRACT

Since an e-mail has been an important mean of communication and information sharing, there have been much effort to classify e-mails efficiently by their contents. An e-mail has various forms in length and style, and words used in an e-mail are usually irregular. In addition, the criteria of an e-mail classification are subjective. As a result, it is quite difficult for the conventional text classification technique to be adapted to an e-mail classification efficiently. An e-mail classification technique in a commercial e-mail program uses a simple text filtering technique in an e-mail client. In the previous studies on automatic classification of an e-mail, the Naive Bayesian technique based on the probability has been used to improve the classification accuracy, and most of them are on an e-mail in English. This paper proposes the personalized recommendation technique of an email in Korean using a data mining technique of frequent patterns. The proposed technique consists of two phases such as the pre-processing of e-mails in an e-mail folder and the generating a profile for the e-mail folder. The generated profile is used for an e-mail to be classified into the most appropriate e-mail folder by the subjective criteria. The e-mail classification system is also implemented, which adapts the proposed technique.

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