Generating Linear Extensions Fast
SIAM Journal on Computing
Global partial orders from sequential data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Comparison of interestingness functions for learning web usage patterns
Proceedings of the eleventh international conference on Information and knowledge management
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Reliable Detection of Episodes in Event Sequences
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Detection of Significant Sets of Episodes in Event Sequences
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Discovering Frequent Closed Partial Orders from Strings
IEEE Transactions on Knowledge and Data Engineering
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Ranking sequential patterns with respect to significance
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Mining frequent partial orders from a collection of sequences was introduced as an alternative to mining frequent sequential patterns in order to provide a more compact/understandable representation. The motivation was that a single partial order can represent the same ordering information between items in the collection as a set of sequential patterns (set of totally ordered sets of items). However, in practice, a discovered set of frequent partial orders is still too large for an effective usage. We address this problem by proposing a method for ranking partial orders with respect to significance that extends our previous work on ranking sequential patterns. In experiments, conducted on a collection of visits to a website of a multinational technology and consulting firm we show the applicability of our framework to discover partial orders of frequently visited webpages that can be actionable in optimizing effectiveness of web-based marketing.