A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis. [PDF]
Entropy (Basel), 2023 Li D, Cheng X.
europepmc +1 more source
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
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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 Closed Itemsets for Coherent Rules: An Inference Analysis Approach [PDF]
, 2011 Past observations have shown that a frequent item set mining algorithm are alleged to mine the closed ones because the finish offers a compact and a whole progress set and higher potency.
Prof. S.Ramakrishna +1 more
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LCTree-Based Approach for Mining Frequent Items in Real-Time. [PDF]
Comput Intell Neurosci, 2022 Chen J +4 more
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Research Track Poster CFI-Stream: Mining Closed Frequent Itemsets in Data Streams
, 2008 Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent closed itemsets, but they are mainly intended for traditional transaction ...
Nan Jiang
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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
TKFIM: Top-K frequent itemset mining technique based on equivalence classes. [PDF]
PeerJ Comput Sci, 2021 Iqbal S +5 more
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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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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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