Results 101 to 110 of about 2,597 (181)
IEEE AccessTraditional pattern mining algorithms are based on tree and linked list structures. However, they often only consider a single factor of frequency or utility and have to deal with exponential search spaces as well as generate numerous candidates.
Xiumei Zhao, Xincheng Zhong, Bing Han
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Mining frequent itemsets using the N-list and subsume concepts
, 2016 Frequent itemset mining is a fundamental element with respect to many data mining problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a new algorithm for mining frequent itemsets based on the idea of N-lists, which ...
Coenen, F, Vo, B, Le, T, Hong, TP
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Mining frequent itemsets a perspective from operations research
Many papers on frequent itemsets have been published. Besides somecontests in this field were held. In the majority of the papers the focus ison speed. Ad hoc algorithms and datastructures were introduced.
Kosters, W.A., Pijls, W.H.L.M.
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Ameliorated Algorithm to Maintain Discovered Frequent Itemsets
, 2001 It is an important task in data mining to maintain discovered frequent itemsets for association rule mining. Because most time-consuming operation for mining association rules is to find the frequent itemsets from the transaction database.
Makinouchi, Akifumi +3 more
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Fast Distributed Algorithm of Mining Global Frequent Itemsets
, 2011 Most distributed algorithms of mining global frequent itemsets worked on net structure network and adopted Apriori-like algorithm. Whereas there were some problems in these algorithms: a lot of candidate itemsets and heavy communication traffic.
Bo He
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arules - A Computational Environment for Mining Association Rules and Frequent Item Sets
Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.
Bettina Grün +2 more
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Moment: Maintaining closed frequent itemsets over a stream sliding window
, 2004 This paper considers the problem of mining closed frequent itemsets over a sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent ...
Philip S. Yu +3 more
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Frequent itemset mining in big data with effective single scan algorithms
, 2018 © 2013 IEEE. This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel ...
Djenouri, Youcef +4 more
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Inverted Index Automata Frequent Itemset Mining for Large Dataset Frequent Itemset Mining
IEEE AccessFrequent itemset mining (FIM) faces significant challenges with the expansion of large-scale datasets. Traditional algorithms such as Apriori, FP-Growth, and Eclat suffer from poor scalability and low efficiency when applied to modern datasets characterized by high dimensionality and high-density features.
Xin Dai 0007 +3 more
openaire +2 more sources
Efficient Frequent Itemsets Mining by Sampling
, 2008 . As the first stage for discovering association rules, frequent itemsets mining is an important challenging task for large databases. Sampling provides an efficient way to get approximating answers in much shorter time.
Chengqi Zhang +2 more
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