Results 41 to 50 of about 659,904 (212)
DISCOVERING CONFUSING FREQUENT ITEMSETS
Tạp chí Khoa học Đại học Đà Lạt, 2018 Frequent itemset mining is one of the most important research areas in the field of association rule mining. Exploiting frequent itemsets at different abstraction levels of data will yield valuable knowledge.
Huỳnh Thành Lộc
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Knowledge Engineering and Data Science, 2020 Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset.
Adie Wahyudi Oktavia Gama +1 more
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A Robust Technique for Closed Frequent and High Utility Itemsets Mining: Closed-FHUIM
IEEE AccessFrequent itemset mining (FIM) and high utility itemset mining (HUIM) are popular data mining techniques used in various real-world applications such as retail-market, bio-medicine, and click-stream analysis.
Muhammad Waheed Ashraf +2 more
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Fire and Materials, EarlyView.ABSTRACT Association rule mining was used to identify patterns in accidental dwelling fire incidents attended by Greater Manchester Fire and Rescue Service over the period 2013/14 to 2023/24. The association rule mining process identified relationships between cooking fire incidence, distraction, living alone, deprivation, and fire injury.
M. Taylor +5 more
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Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, 2016 AbstrakAlgoritma yang umum digunakan dalam proses pencarian frequent itemset (data yang paling sering muncul) adalah Apriori. Tetapi Algoritma Apriori mempunyai memiliki kekurangan yaitu membutuhkan waktu yang lama dalam proses pencarian frequent itemset.
Wirdah Choiriah
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Parallel Mining Algorithm for the Enumeration Space of Closed High Utility Itemsets
Journal of Harbin University of Science and TechnologyTo address the issues of result redundancy and time overhead in high-dimensional data environments, a closed high utility itemset mining algorithm, SpCHUIM (Closed High Utility Itemsets Mining on Spark), is proposed.
LI Chengyan, SUN Anqi, LIU Songlin
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TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns
IEEE Access, 2018 High-utility itemset mining (HUIM) has been gaining popularity in the field of data mining. Frequent itemset mining used to be the main tool to reveal high-frequency patterns but failed to consider the concept of profit.
Jimmy Ming-Tai Wu +3 more
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Efficient heuristics for the Steiner forest problem
International Transactions in Operational Research, EarlyView.Abstract Let G=(V,E)$G = (V, E)$ be a connected undirected graph, V$V$ a set of nodes, E$E$ a set of edges, |V|=n$|V| = n$, and |E|=m$|E| = m$. Given a non‐negative weight function w:E→R+$w: E \rightarrow \mathbb {R}^+$ associated with its edges, a set τ={Ti⊆V|i=1,…,p}$\tau = \lbrace T_i \subseteq V | i = 1, \ldots, p\rbrace$ of terminal sets Ti$T_i ...
Murilo Stockinger +4 more
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A study of selected itemset mining and association rule mining algorithms
, 2022 Data Mining is a central step in Knowledge Discovery in Databases (KDD). This thesis is within the context of Data Mining and more specifically revolves around a couple the Data Mining tasks: Itemset Mining and Association Rule Mining.
Hammami, Rim
core
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.AI and Big Data in Consumer Behavior Analysis. ABSTRACT The rapid expansion of digital consumer data has challenged traditional approaches to understanding behavior in digital marketing. Existing reviews often focus on individual methods and give limited guidance on how analytical techniques compare or how they should be selected for specific marketing
Leonidas Theodorakopoulos +1 more
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