Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The String-to-String Correction Problem
Journal of the ACM (JACM)
Comparing Hierarchical Data in External Memory
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering frequently changing structures from historical structural deltas of unordered XML
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Mining history of changes to web access patterns
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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In this paper, we firstly investigate the possible changes on web usage behaviors and then propose an x-tracking method to detect these changes. The changes on web navigation patterns are depicted from microscopic and macroscopic levels: the former is for the "internal" and "external" changes which show the variations on the semantics and external physical features respectively, while the latter is modeled for the changes of the popularity on "local" and "global" time line. The x-tracking method we propose is to detect the newly emerged patterns (EP) based on "internal" feature, which is the premise to compute the changes on other features by tracking their internal unchanged patterns (IUP). Experiments show that the x-tracked changes are condensed, efficient and informative.