Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
A Fast Algorithm for Mining Rare Itemsets
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets
Data Mining and Knowledge Discovery
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In this paper, we present an algorithm for mining frequent itemsets in a stream of transactions within a limited time horizon. In contrast to other approaches that are presented in the literature, the proposed algorithm makes use of a test window that can discard non-frequent itemsets from a set of candidates. The efficiency of this approach relies on the property that the higher the support threshold is, the smaller the test window is. In addition to considering a sharp horizon, we consider a smooth window. Indeed, in many applications that are of practical interest, not all of the time slots have the same relevance, e.g., more recent slots can be more interesting than older slots. Smoothness can be determined in both qualitative and quantitative terms. A comparison to other algorithms is conducted. The experimental results prove that the proposed solution is faster than other approaches but has a slightly higher cost in terms of memory.