AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY ITEMSETS
Tạp chí Khoa họcHigh utility itemsets (HUIs) mining is the finding of itemsets that satisfy a user-defined minimum utility threshold. Many successful studies in this field have been carried out, however they are all reliant on Tidset techniques, which records the ...
Nguyen Thi Thanh Thuy*, Nguyen Van Le, Manh Thien Ly
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A Further Study in the Data Partitioning Approach
, 2008 Frequent itemsets mining is well explored for various data types, and its computational complexity is well understood. Based on our previous work by Nguyen and Orlowska (2005), this paper shows the extension of the data pre-processing approach to further
Son N. Nguyen +2 more
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Mining frequent itemsets using the N-list and subsume concepts [PDF]
, 2016 Frequent itemset mining is a fundamental element with respect to many data mining problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a new algorithm for mining frequent itemsets based on the idea of N-lists, which ...
Coenen, F, Vo, B, Le, T, Hong, TP
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On Discovering Discriminative Itemsets Based on Detecting Frequent Itemsets in Succession
AlgorithmsDiscovery of Association Rules is one of the most common Data Mining techniques. Contrast data mining is a focused data mining research area for discovering interesting contrast patterns that state the significant differences between datasets, i.e ...
Aristotelis Kompothrekas +1 more
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The Mining Algorithm of Maximum Frequent Itemsets Based on Frequent Pattern Tree. [PDF]
Comput Intell Neurosci, 2022 Mi X.
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cdeNDI:a Efficient Algorithm for Mining Frequent Itemsets
, 2007 頻繁項目集的探勘，也就是從大型資料庫中找出頻繁項目集。這是許多其他問題的根本和基礎，像是關連規則、循序規則、分類和許多其他的課題。 在過去十年來，這個問題已經有了很大的進展。許多的演算法或改進現有演算法都不斷的被提出。然而，當我們降低最低支持度或是當我們遇到的資料庫是高度關連的時候，頻繁項目集的數目可能會極大。因此，如何應付密集資料庫仍然是一各具挑戰性的課題。 在這篇論文裡，我們提出cdeNDI這一種新演算法。這是以Eclat這個演算法為基礎，將closed itemsets和non ...
Huang, Chien-Ming, 黃健銘
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GPU-Accelerated Apriori Algorithm
ITM Web of Conferences, 2017 This paper propose a parallel Apriori algorithm based on GPU (GPUApriori) for frequent itemsets mining, and designs a storage structure using bit table (BIT) matrix to replace the traditional storage mode. In addition, parallel computing scheme on GPU is
Jiang Hao +3 more
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New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
Algorithms, 2018 The Map-Reduce (MR) framework has become a popular framework for developing new parallel algorithms for Big Data. Efficient algorithms for data mining of big data and distributed databases has become an important problem.
Yaron Gonen, Ehud Gudes, Kirill Kandalov
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Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts. [PDF]
Sensors (Basel), 2022 Rehman SU +4 more
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Mining frequent closed itemsets from distributed repositories
, 2007 In this paper we address the problem of mining frequent closed itemsets in a highly distributed setting like a Grid. The extraction of frequent (closed) itemsets is an important problem in Data Mining, and is a very expensive phase needed to extract from
Lucchese C +3 more
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