Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Discovering frequent sets from data streams with CPU constraint
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
A survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
Online mining of frequent sets in data streams with error guarantee
Knowledge and Information Systems
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
A generic approach for mining indirect association rules in data streams
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Anomaly intrusion detection based on clustering a data stream
ISC'06 Proceedings of the 9th international conference on Information Security
Maintaining frequent itemsets over high-speed data streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
EStream: online mining of frequent sets with precise error guarantee
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
User subjectivity in change modeling of streaming itemsets
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Mining frequent itemsets over tuple-evolving data streams
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
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Knowledge embedded in a data stream is likely to be changed as time goes by. Consequently, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window-based method that finds recently frequent itemsets over an online data stream adaptively. The size of a window defines a desired life-time of the information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the frequent itemsets of a data stream.