Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
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
GC-tree: a fast online algorithm for mining frequent closed itemsets
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Finding Frequent Closed Itemsets in Sliding Window in Linear Time
IEICE - Transactions on Information and Systems
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Finding Association Rules is a classical data mining task. The most critical part of Association Rules Mining is about finding the frequent itemsets in the database. Since the introduction of the famouse Apriori algorithm [14], many others have been proposed to find the frequent itemsets. Among all the algorithms, the approach of mining closed itemsets has arisen a lot of interests in data mining community, because the closed itemsets are the condensed representation of all the frequent itemsets. The algorithms taking this approach include TITANIC [8], CLOSET+ [6], DCI-Closed [4], FCI-Stream [3], GC-Tree [15], etc. While the above algorithms are trying to improve the performance of finding the Intents of Formal Concepts (in anther word, the closed itemsets), they missed another important information: the Extents of Formal Concepts. In this paper, we propose an online algorithm, TGC-Tree, which is adapted from the GC-Tree algorithm [15], that could be used to trace the closed itemsets(Intents) and the corresponding transaction sets(Extents) simultaneously in an incremental way.