Automated Ham-Spam Lexicon Generation Based on Semantic Relations Extraction

Literature Review

Mining Techniques are currently the key factor for different research in different fields including healthcare systems (Khedr, Kholeif, & Saad, 2017) and education systems (Khedr, Kholeif, & Hessen, 2015). Manipulating these techniques has proved to provide high accuracy in the discovery process such as in (Sultan, Khedr, Idrees, & Kholeif, 2017) which proposed an enhancement in FP-growth associations rules mining algorithm. Other examples are presented in (Khedr, Idrees, & Elseddawy, 2016; Khedr & Borgman, 2006) which proposed a successful enhancement classification mining techniques. Segments of information with different nature are targeted due to the variety of their usage such as in (Khedr & Kok, 2006) for banking data, (Khedr, 2013) for risk management data, and (Khedr, Kholeif, & Hessen, 2015) for learning data. In this section, the research will focus on two consecutive subjects, text analysis with demonstrating its applications and related fields, then a focus on the research for detecting spams from e-mails is discussed.