Results 31 to 40 of about 1,488,653 (205)
Efficiently Mining Frequent Itemsets on Massive Data
IEEE Access, 2019 Frequent itemset mining is an important operation to return all itemsets in the transaction table, which occur as a subset of at least a specified fraction of the transactions.
Xixian Han +5 more
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A Hybrid Approach for Mining Frequent Itemsets [PDF]
2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013 Frequent item set mining is a fundamental element with respect to many data mining problems. Recently, the PrePost algorithm has been proposed, a new algorithm for mining frequent item sets based on the idea of N-lists. PrePost in most cases outperforms other current state-of-the-art algorithms.
Bay Vo +3 more
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A weighted frequent itemset mining algorithm for intelligent decision in smart systems
IEEE Access, 2018 Intelligent decision is the key technology of smart systems. Data mining technology has been playing an increasingly important role in decision-making activities.
Xuejian Zhao +4 more
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Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data
IEEE Access, 2018 Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy ...
Xinyu Xiong +6 more
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Parallel algorithms for mining of frequent itemsets
CoRR, 2021 In the recent decade companies started collecting of large amount of data. Without a proper analyse, the data are usually useless. The field of analysing the data is called data mining. Unfortunately, the amount of data is quite large: the data do not fit into main memory and the processing time can become quite huge.
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Incremental Closed Frequent Itemsets Mining-Based Approach Using Maximal Candidates
IEEE AccessIncremental frequent itemset mining aims to efficiently update frequent itemsets without recalculating them from scratch, making it suitable for streaming data and real-time analytics.
Mohammed A. Al-Zeiadi +1 more
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EFFICIENT FREQUENT ITEMSET DISCOVERY THROUGH HIERARCHICAL HUFFMAN ENCODING [PDF]
ICTACT Journal on Soft ComputingFrequent itemsets mining holds a crucial position in the field of data mining; however, traditional algorithms like Apriori and FP-Growth often encounter efficiency and memory consumption issues when handling large-scale datasets, which not only makes ...
Dai Xin, Hao Xue
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Proposed Algorithm for Extracting Association Rule Depend on Closed Frequent Itemset (EACFI) [PDF]
Engineering and Technology Journal, 2011 Association rules are important one of data mining activities. All algorithms of association rule mining consist of finding frequency of itemsets, which satisfy a minimum support threshold, and then compute confidence percentage for each k-itemsets to ...
Emad k. Jbbar, Yaser Munther
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An Efficient Spark-Based Hybrid Frequent Itemset Mining Algorithm for Big Data
Data, 2022 Frequent itemset mining (FIM) is a common approach for discovering hidden frequent patterns from transactional databases used in prediction, association rules, classification, etc. Apriori is an FIM elementary algorithm with iterative nature used to find
Mohamed Reda Al-Bana +2 more
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CLOLINK: An Adapted Algorithm for Mining Closed Frequent Itemsets [PDF]
, 2012 Mining of the complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of closed frequent itemsets, which results in a much smaller number of itemsets.
Onashoga, Adebukola, Adebukola Onashoga
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