Frequent pattern mining for community dedection in web logs: Group based habit dedection in community using network traces

Abstract

During last 10 years, internet usage network has spread at an unforeseen rate. Concurrently, many services including official transactions have been granted from the internet. By the way, it has become an area where internet users have access to apps and interact with each other over social media. People's internet usage habits have become a domain that can give information about the areas of the population and their interests. In social networks, group analysis and connection predictions are popular terms that construct base of a lot of scientific works. This terms, especially, used for; ads customize, habits and tendency retain or determination of friendship based on same character. Network users can be grouped according to their activity and character, or they can be grouped according to their movement over the network by their estimation algorithms. In this study, all dataset filtered to get anonymous traffic logs that generated by a part of internet users who are in the network of an institution and the community was explored. All logs analyzed according to Category-based diversity, time periods of accesses and usage periods. As expected, communities were established from the habits of users with "Pattern Based Frequency Analysis Method". Voluntary experimental users whose mac addresses were already defined and belong to determined groups redistributed with specified method. Lastly, via calculating the achievement of correct distribution results the success of method found out. © 2017 IEEE.