, 2010 The subject of this research is mining data stream. It is one of the most challenging and widely researched areas in Knowledge Discovery and Data Mining (KDD). A data stream is a continuous, voluminous, and unpredictable flow of data which occurs in many
Viriyarattanaporn, Nathaphong
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An Efficient Subset-Lattice Algorithm for Mining Closed Frequent Itemsets in Data Streams
, 2009 Online mining association rules over data streams is an important issue in the area of data mining, where an association rule means that the presence of some items in a transaction will imply the presence of other items in the same transaction. There are
Peng, Wei-hau
core
Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns. [PDF]
Entropy (Basel), 2021 Zhou S +7 more
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DARCI: Distributed Association Rule Mining Utilizing Closed Itemsets
, 2014 A distributed rule mining algorithm must minimize the communication cost to reduce the communication bandwidth use and to improve the scalability. There are a few distributed rule mining algorithms reported in the literature.
Suad Alramouni, Jae Young Lee
core
Design and Implementation of a New Local Alignment Algorithm for Multilayer Networks. [PDF]
Entropy (Basel), 2022 Milano M, Guzzi PH, Cannataro M.
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Mining actionable combined high utility incremental and associated sequential patterns. [PDF]
PLoS One, 2023 Shi M, Gong Y, Xu T, Zhao L.
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Pruning closed itemset lattices for association rules
, 1998 Discovering association rules is one of the most important task in data mining and many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning of the subset lattice (itemset lattice).
Pasquier, Nicolas +3 more
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An Efficient Algorithm for Mining Closed Frequent Inter-transaction Itemsets
, 2007 跨交易關聯規則可代表不同交易中項目間的關係，而近年來有愈來愈多相關的探勘演算法被提出，然而這些演算法會產生相當多的跨交易頻繁項目集合。找尋封閉性跨交易頻繁項目集合可使探勘的過程更有效率。 因此，在本篇論文中我們提出了一個探勘演算法叫「ICMiner」，以找尋封閉性跨交易頻繁項目集合。我們的方法可分為兩個階段。第一階段，將原始的資料庫轉換成領域屬性集合，使得每一個頻繁項目的領域屬性形成一個集合。第二階段，利用ID-tree去列舉出所有的封閉性跨交易頻繁項目集合。藉由ID-tree進行資料探勘 ...
翁婉玉, Weng, Wan-Yu
core
Innovative Mode of Human Resource Management of University Teachers Based on Intelligent Big Data Analysis. [PDF]
Comput Intell Neurosci, 2022 Bai Y.
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OLOGRAM-MODL: mining enriched n-wise combinations of genomic features with Monte Carlo and dictionary learning. [PDF]
NAR Genom Bioinform, 2021 Ferré Q, Capponi C, Puthier D.
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