A Clustering Technique for Email Content Mining

In today's world of internet, with whole lot of e-documents such, as html pages, digital libraries etc. occupying considerable amount of cyber space, organizing these documents has become a practical need. Clustering is an important technique that organizes large number of objects into smaller coherent groups. This helps in efficient and effective use of these documents for information retrieval and other NLP tasks. Email is one of the most frequently used e-document by individual or organization. Email categorization is one of the major tasks of email mining. Categorizing emails into different groups help easy retrieval and maintenance. Like other e-documents, emails can also be classified using clustering algorithms. In this paper a similarity measure called Similarity Measure for Text Processing is suggested for email clustering. The suggested similarity measure takes into account three situations: feature appears in both emails, feature appears in only one email and feature appears in none of the emails. The potency of suggested similarity measure is analyzed on Enron email data set to categorize emails. The outcome indicates that the efficiency acquired by the suggested similarity measure is better than that acquired by other measures.