EMail Data Mining: An Approach to Construct an Organization Position-wise Structure While Performing EMail Analysis

In this age of social networking, it is necessary to define the relationships among the members of a social network. Various techniques are already available to define user- to-user relationships across the network. Over time, many algorithms and machine learning techniques were applied to find relationships over social networks, yet very few techniques and information are available to define a relation directly over raw email data. Few educational societies have developed a way to mine the email log files and have found the inter-relation between the users by means of clusters. Again, there is no solid technique available that can accurately predict the ranking of each user within an organization by mining through their email transaction logs. The author in this report presents a technique to mine the email data log files in order to figure out the position wise structure of an organization. The author also discusses send-receive analysis, statistical analysis, semantic analysis and temporal analysis over the data, and has applied them to test cases. Throughout the research the author has used the Enron employees email log files, which was made public on 2001