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On a visual frequent itemset mining
2009 Fourth International Conference on Digital Information Management, 2009Given a large, dense transaction database, generating interesting frequent patterns in a user friendly manner remains as an important issue in data mining. It is because the minimum support, the most popular statistical significance measurement, is not capable of reflecting the domain user's interest. This paper presents visual frequent itemset mining (
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Mining Frequent and Homogeneous Closed Itemsets
2016It is well known that when mining frequent itemsets from a transaction database, the output is usually too large to be effectively exploited by users. To cope with this difficulty, several forms of condensed representations of the set of frequent itemsets have been proposed, among which the notion of closure is one of the most popular.
Inès Hilali +4 more
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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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2019
We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Cafaro, Massimo, Pulimeno, Marco
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We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Cafaro, Massimo, Pulimeno, Marco
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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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FPGA/GPU-based Acceleration for Frequent Itemsets Mining: A Comprehensive Review
ACM Computing Surveys, 2022Lazaro Bustio-Martínez +2 more
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