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Mining Maximal Frequent Itemsets with Frequent Pattern List
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Mining frequent itemsets is a major aspect of association rule research. However, the mining of the complete of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets.
Jin Qian, Feiyue Ye
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Generating Closed Frequent Itemsets with the Frequent Pattern List
2010 2nd International Workshop on Database Technology and Applications, 2010An approach is proposed to discover closed frequent itemsets with a simple linear list structure called the Frequent Pattern List(FPL) in transaction database. The approach selects representation patterns from candidate itemsets to reduce combinational space of frequent patterns.
Qin Li, Sheng Chang
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Discovering Frequent Itemsets in the Presence of Highly Frequent Items
2003This paper presents new techniques for focusing the discovery of frequent itemsets within large, dense datasets containing highly frequent items. The existence of highly frequent items adds significantly to the cost of computing the complete set of frequent itemsets.
Dennis P. Groth, Edward L. Robertson
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The Parameterized Complexity of Enumerating Frequent Itemsets
2006A core problem in data mining is enumerating frequently-occurring itemsets in a given set of transactions. The search and enumeration versions of this problem have recently been proven NP- and #P-hard, respectively (Gunopulos et al, 2003) and known algorithms all have running times whose exponential terms are functions of either the size of the largest
Matthew Hamilton +2 more
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Memory Efficient Frequent Itemset Mining
2018Frequent itemset mining has been one of the most popular data mining techniques. Despite a large number of algorithms developed to implement this functionality, there is still room for improvement of their efficiency. In this paper, we focus on memory use in frequent itemset mining.
Nima Shahbazi +2 more
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Incremental Frequent Itemsets Mining with MapReduce
2017Frequent itemsets mining is a common task in data mining. Since sizes of today’s databases go far beyond capabilities of a single machine, recent studies show how to adopt classical algorithms for frequent itemsets mining for parallel frameworks such as MapReduce. Even then, in case of a slight database update a re-run of the MapReduce mining algorithm
Kirill Kandalov, Ehud Gudes
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Efficient weighted probabilistic frequent itemset mining in uncertain databases
Expert Systems, 2021Zhiyang Li, Junfeng Wu, Zhaobin Liu
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HBPFP-DC: A parallel frequent itemset mining using Spark
Parallel Computing, 2021Yaling Xun, Jifu Zhang, Haifeng Yang
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Frequent itemset mining: A 25 years review
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2019JOSÉ Maria Luna +2 more
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