A new approach for association rule mining and bi-clustering using formal concept analysis
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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Frequent closed itemsets (FCI) is a condensed representation method for frequent item-sets. FCI reduces the redundant rules and increases the performance of mining. In recent years, a large number of algorithms have been proposed about frequent closed itemsets mining due to the importance of them In this paper, we generally review and compare the most important FCI algorithms with each other. Results show that each algorithm based on its applied strategy has some advantages and disadvantages for mining in dense and sparse datasets. However, DCI-Closed algorithm is more effective than other ones.