An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Top Down FP-Growth for Association Rule Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
An approximate approach for mining recently frequent itemsets from data streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
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Catching the recent trend of data is an important issue when mining frequent itemsets from data streams. To prevent from storing the whole transaction data within the sliding window, the frequency changing point (FCP) method was proposed for monitoring the recent occurrences of itemsets in a data stream under the assumption that exact one transaction arrives at each time point. In this paper, the FCP method is extended for maintaining recent patterns in a data stream where a block of various numbers of transactions (including zero or more transactions) is inputted within each time unit. Moreover, to avoid generating redundant information in the mining results, the recently representative patterns are discovered from the maintained structure approximately. The experimental results show that our approach reduces the run-time memory usage significantly. Moreover, the proposed GFCP algorithm achieves high accuracy of mining results and guarantees no false dismissal occurring.