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Parallel Mining Algorithm of Frequent Itemset Based on N-list and DiffNodeset Structure [PDF]
Jisuanji kexue, 2023 Frequent itemset mining is a basic problem of data mining and plays an important role in many data mining applications.In order to solve the problems of the parallel frequent itemset mining algorithm(MrPrePost) in big data environment,such as algorithm ...
ZHANG Yang, WANG Rui, WU Guanfeng, LIU Hongyi
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Mining Frequent Itemsets in a Stream [PDF]
Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007 We study the problem of finding frequent itemsets in a continuous stream of transactions. The current frequency of an itemset in a stream is defined as its maximal frequency over all possible windows in the stream from any point in the past until the current state that satisfy a minimal length constraint.
Toon Calders +2 more
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A review on big data based parallel and distributed approaches of pattern mining
Journal of King Saud University: Computer and Information Sciences, 2022 Pattern mining is a fundamental technique of data mining to discover interesting correlations in the data set. There are several variations of pattern mining, such as frequent itemset mining, sequence mining, and high utility itemset mining. High utility
Sunil Kumar, Krishna Kumar Mohbey
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A Bitmap Approach for Mining Erasable Itemsets
IEEE Access, 2021 Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of ...
Tzung-Pei Hong +4 more
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An Efficient Spark-Based Hybrid Frequent Itemset Mining Algorithm for Big Data
International Conference on Data Technologies and Applications, 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
semanticscholar +1 more source
Efficient Associate Rules Mining Based on Topology for Items of Transactional Data
Mathematics, 2023 A challenge in association rules’ mining is effectively reducing the time and space complexity in association rules mining with predefined minimum support and confidence thresholds from huge transaction databases.
Bo Li, Zheng Pei, Chao Zhang, Fei Hao
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Krimp: mining itemsets that compress [PDF]
Data Mining and Knowledge Discovery, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jilles Vreeken +2 more
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Non-derivable itemset mining [PDF]
Data Mining and Knowledge Discovery, 2007 All frequent itemset mining algorithms rely heavily on the monotonicity principle for pruning. This principle allows for excluding candidate itemsets from the expensive counting phase. In this paper, we present sound and complete deduction rules to derive bounds on the support of an itemset.
Toon Calders, Bart Goethals
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Dominance Programming for Itemset Mining [PDF]
2013 IEEE 13th International Conference on Data Mining, 2013 Finding small sets of interesting patterns is an important challenge in pattern mining. In this paper, we argue that several well-known approaches that address this challenge are based on performing pair wise comparisons between patterns. Examples include finding closed patterns, free patterns, relevant subgroups and skyline patterns. Although progress
Negrevergne, Benjamin +4 more
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Modern Applications and Challenges for Rare Itemset Mining
, 2021 Data mining is the process of extracting useful unknown knowledge from large datasets. Frequent itemset mining is the fundamental task of data mining that aims at discovering interesting itemsets that frequently appear together in a dataset.
Sadeq Darrab, David Broneske, G. Saake
semanticscholar +1 more source