Results 41 to 50 of about 1,488,747 (207)
Generic Itemset Mining Based on Reinforcement Learning
IEEE Access, 2022 One of the biggest problems in itemset mining is the requirement of developing a data structure or algorithm, every time a user wants to extract a different type of itemsets.
Kazuma Fujioka, Kimiaki Shirahama
doaj +1 more source
Parallel algorithms for mining of frequent itemsets
CoRR, 2021 In the recent decade companies started collecting of large amount of data. Without a proper analyse, the data are usually useless. The field of analysing the data is called data mining. Unfortunately, the amount of data is quite large: the data do not fit into main memory and the processing time can become quite huge.
openaire +2 more sources
, 2017 Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear ...
Jerry Chun‐Wei Lin +11 more
core +1 more source
The MapReduce Model on Cascading Platform for Frequent Itemset Mining
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2018 The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models.
Nur Rokhman, Amelia Nursanti
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SECURE ASSOCIATION RULE MINING ON VERTICALLY PARTITIONED DATA USING FULLY HOMOMORPHIC ENCRYPTION
ICTACT Journal on Soft Computing, 2021 Cloud Computing is a leading innovation technology that guides to access applications over the web. The data owner’s data can be gotten to and controlled in the cloud.
M Yogasini, B N Prathibha
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Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
Applied Sciences, 2021 When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets.
Yalong Zhang +4 more
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Frequent itemset mining in high dimensional data: a review [PDF]
, 2018 This paper provides a brief overview of the techniques used in frequent itemset mining. It discusses the search strategies used; i.e. depth first vs. breadth-first, and dataset representation; i.e. horizontal vs. vertical representation.
Nurul Fariza Zulkurnain +3 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
wiley +1 more source
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
wiley +1 more source
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
wiley +1 more source