On computing, storing and querying frequent patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
A new approach to mine frequent patterns using item-transformation methods
Information Systems
A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
Data & Knowledge Engineering
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
On pushing weight constraints deeply into frequent itemset mining
Intelligent Data Analysis
Mining frequent patterns from network flows for monitoring network
Expert Systems with Applications: An International Journal
WLPMiner: weighted frequent pattern mining with length-decreasing support constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mop: An Efficient Algorithm for Mining Frequent Pattern with Subtree Traversing
Fundamenta Informaticae
The movie mashup application MoMa: geolocalizing and finding movies
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
Information Sciences: an International Journal
Recommendations of closed consensus temporal patterns by group decision making
Knowledge-Based Systems
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Mining frequent patterns is a fundamental and important problem in many data mining applications. Many ofthe algorithms adopt the pattern growth approach, whichis shown to be superior to the candidate generate-and-test approach significantly. In this paper, we identify thekey factor that influence the performance of the patterngrowth approach, and optimize them to further improvethe performance. Our algorithm uses a simple while compact data structure-ascending frequency ordered prefix-tree(AFOPT) to organize the conditional databases, inwhich we use arrays to store single branches to further savespace. We traverse our prefix-tree structure using a top-down strategy. Our experiment results show that the combination of the top-down traversal strategy and the ascendingfrequency item ordering method achieves significant performance improvement over previous works.