A Framework of Email Cleansing and Mining With Case Study on Image Spamming

“EMAIL CLEANSING” deals with process of eliminating irrelevant non-text data (it includes header, signature, quotation and program code filtering) and transforming relevant text data into canonical form (which includes word, sentence and paragraph normalization). Many text mining applications need to take emails as input. Email data is usually noisy and thus it is necessary to clean it before mining. Email text mining is one of the major parts of email processing. The main purpose of email text mining are Statistical Learning, determining the importance of the email, determine whether the email is spam or not etc. In this paper we are going to address the issue of email cleansing for text filtering as well as spamming based upon text filtering and image filtering.