Data Mining for Spam Email Classification
Gitae Kim · Asia-pacific Journal of Multimedia services convergent with Art Humanities and Sociology · 2016
This paper proposes a classification model for spam email using data mining. The use of personal or business email has increased along with the growth of internet population and computerization. This change allows one to live in convenience or to improve the efficiency of business. On the other hand, spam emails cause many problems in our life. Spam email is defined as the email which is sent to anyone who does not want to receive the email that brings to annoyance or computer virus or interruption of business process. Although a lot of studies have been proposed to protect the spam email, we are still in need of an efficient classification method. Data mining is a prominent way to classify spam emails. However, if data do not have sufficient information, traditional data mining method may not apply for the problem. Therefore, we suggest PU learning algorithm to classify the problem with insufficient data which have only positive class and unlabeled data. Support vector machine (SVM) has been used as the basic data mining method. Experimental results show the viability of the proposed classification model.