POISSON FREQUENT PATTERNS CLUSTERING AND TEMPORAL SIMILARITY TRAFFIC MINING FOR WEB USER TRACKING
Journal Title: IJAR-Indian Journal of Applied Research - Year 2018, Vol 8, Issue 8
Abstract
Web traffic pattern analysis is significant to find the web user behaviors. Few research works has been developed for web traffic pattern mining and web user tracking. However, computational complexity taken for tracking user location of web traffic patterns was higher. In order to overcome such limitation, Poisson Fragment Frequency based Web Pattern Clustering (PFF-WPC) technique is proposed. The PFF-WPC technique is designed with objective of reducing the computational complexity of identifying the location of user interest web pages from a weblog database. Initially PFF-WPC technique performs Poisson Fragment Process with aiming at grouping the web pages in a weblog database according to number of sessions. After session identification, PFF-WPC technique performs Frequency based web patterns clustering with objective of grouping the web pages in an each session as frequent or non frequent web pages with higher clustering accuracy. Finally, PFF-WPC technique carried outs the Temporal Similarity Based Web User Tracking process in which traffic web patterns are detected based on the measurement of temporal similarity among the sessions. Based on identified traffic web patterns, the location of corresponding web users is tracked with help of public IP address stored in weblog database with minimum computational complexity. The PFF-WPC technique conducts the experimental evaluations on factors such as clustering efficiency, computational complexity, true positive rate and space complexity. The experimental result reveals that PFF-WPC technique is able to improve the true positive rate and also reduces computational complexity of web user tracking when compared to state-of-the-art-works.
Authors and Affiliations
Ulaganathan. n, Prasath. s
Keywords
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