Results 101 to 110 of about 846,106 (226)
A False Negative Maximal Frequent Itemsets Mining Algorithm over Stream
, 2011 Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining.
Ning Zhang, Hai Feng Li
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Class Association Rule Pada Metode Associative Classification
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2011 Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining. Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list ...
Eka Karyawati, Edi Winarko
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DiffNodesets: An efficient structure for fast mining frequent itemsets
, 2016 Mining frequent itemsets is an essential problem in data mining and plays an important role in many data mining applications. In recent years, some itemset representations based on node sets have been proposed, which have shown to be very efficient for ...
Deng, Zhi-Hong
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Frequent Itemsets Mining with Chemical Reaction Optimization Metaheuristic
, 2018 International audienceFrequent Itemsets mining is a key concept in Association Rule Mining task, it aims to discover the frequent itemsets in a transactional dataset.Nowadays large amounts of data needs to be analysed, thus the use of traditional ...
Abdesslem Layeb +5 more
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maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams
, 2009 IEEEMining frequent itemsets over online data streams, where the new data arrive and the old data will be removed with high speed, is a challenge for the computational complexity.
Li Kun, Wang Hongan, Wang Yongyan
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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 of Global Maximum Frequent Itemsets Based on FP-Tree
, 2011 As far as we know, a little research of mining global maximum frequent itemsets has been done. The paper proposed an algorithm for mining global maximum frequent itemsets based on FP-tree, namely, AMGMFI algorithm.
Bo He
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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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An efficient pattern growth approach for mining fault tolerant frequent itemsets. [PDF]
Expert Syst Appl, 2020 Bashir S, Bashir S.
europepmc +1 more source
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