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Efficiently mining maximal frequent itemsets
Proceedings 2001 IEEE International Conference on Data Mining, 2002 We present GenMax, a backtracking search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation.
Karam Gouda, Mohammed Javeed Zaki
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Mining All Non-derivable Frequent Itemsets [PDF]
, 2002 3 ...
Toon Calders, Bart Goethals
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Frequent Itemset Mining in Large Datasets a Survey
, 2017 Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules.
Manish Kumar, Amrit Pal
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Dynamic Frequent Itemset Mining Based on Matrix Appriori Algorithm
, 2012 The frequent itemset mining algorithms discover the frequent itemsets from a database. When the database is updated, the frequent itemsets should be updated as well. However, running the frequent itemset mining algorithms with every update is inefficent.
Oğuz, Damla
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The Duality of Frequent-Itemset Mining and Erasable-Itemset Mining
, 2019 In data mining, frequent-itemset mining and erasable-itemset mining are two standard and practical techniques for finding useful itemsets. Frequent-itemset mining is a significant pre-processing step in the search for association rules and is mainly ...
Wang, Chun-Ho
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Privacy-preserving Frequent Itemset Mining for Sparse and Dense Data [PDF]
, 2015 . Frequent itemset mining is a task that can in turn be used for other purposes such as associative rule mining. One problem is that the data may be sensitive, and its owner may refuse to give it for analysis in plaintext.
Alisa Pankova, Peeter Laud
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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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An Evolutionary Algorithm to Mine High-Utility Itemsets
Advances in Electrical and Electronic Engineering, 2015 High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) of association rules (ARs). In this
Jerry Chun-Wei Lin +5 more
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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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Frequent itemset mining on multiprocessor systems.
, 2014 Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data.
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