Using conjunction of attribute values for classification
Proceedings of the eleventh international conference on Information and knowledge management
A Confidence-Lift Support Specification for Interesting Associations Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Parallel tree-projection-based sequence mining algorithms
Parallel Computing
Efficient closed pattern mining in the presence of tough block constraints
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Automated support specification for efficient mining of interesting association rules
Journal of Information Science
Discovering frequent geometric subgraphs
Information Systems
Query-sets: using implicit feedback and query patterns to organize web documents
Proceedings of the 17th international conference on World Wide Web
A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
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
Applications of web query mining
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
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Over the years, a variety of algorithms or finding frequentitemsets in very large transaction databases have been developed. The key feature in most to these algorithms is that they use a constant support constraint to control the inherently exponential complexity of the problem. In general, itemsets that contain only a few items will tend to be interesting if they have a high support, whereas long itemsets can still be interesting even if their support is relatively small. Ideally, we desire to have an algorithm that finds all the frequent itemsets whose support decreases as a function of their length. In this paper we present an algorithm called LPMiner, that finds all itemsets that satisfy a length-decreasing support constraint. Our experimental evaluation shows that LPMiner is up to two orders of magnitude faster than the FP-growth algorithm or finding itemsets at a constant support constraint, and that its runtime increasesgradually as the average length of the transactions (and the discovered itemsets) increases.