Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Adaptive and Resource-Aware Mining of Frequent Sets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Frequent Closed Itemsets in Sliding Window in Linear Time
IEICE - Transactions on Information and Systems
TGC-tree: an online algorithm tracing closed itemset and transaction set simultaneously
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
Mining closed itemsets in data stream using formal concept analysis
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
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Frequent closed itemsets is a complete and condensed representaion for all the frequent itemsets, and it's important to generate non-redundant association rules. It has been studied extensively in data mining research, but most of them are done based on traditional transaction database environment and thus have performance issue under data stream environment. In this paper, a novel approach is proposed to mining closed frequent itemsets over data streams. It is an online algorithm which update frequent closed itemsets incrementally, and can output the current closed frequent itemsets in real time based on users specified thresholds. The experimental evaluation shows that our proposed method is both time and space efficient, compared with the state of art online frequent closed itemsets algorithm FCI-Stream [3].