Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
Utility-Based Web Path Traversal Pattern Mining
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
A two-phase algorithm for fast discovery of high utility itemsets
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Fuzzy classification in web usage mining using fuzzy quantifiers
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Mining useful web path traversal patterns is a very important research issue in web technologies. Knowledge about the frequent web path traversal patterns enables us to discover the most interesting websites traversed by the users. However, considering only the binary (presence/absence) occurrences of the websites in the web traversal paths, real world scenarios may not be reflected. Therefore, if we consider the time spent by each user as a utility value of a website, more interesting web traversal paths can be discovered. In this paper, we propose a novel algorithm for utility-based web path traversal pattern mining. Our extensive experimental results show that our algorithm outperforms the existing algorithm.