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Towards Scalable Algorithm for Closed Itemset Mining in High-Dimensional Data
2017
Indonesian Journal of Electrical Engineering and Computer Science
<p>Mining frequent itemsets from large dataset has a major drawback in which the explosive number of itemsets requires additional mining process which might filter the interesting ones. Therefore, as the solution, the concept of closed frequent itemset was introduced that is lossless and condensed representation of all the frequent itemsets and their corresponding supports. Unfortunately, many algorithms are not memory-efficient since it requires the storage of closed itemsets in main memory
doi:10.11591/ijeecs.v8.i2.pp487-494
fatcat:zihsrmbg4rctpn3psyfskxypza