Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts [PDF]
Sensors, 2022 Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies.
Saif Ur Rehman +4 more
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CLTD-LP: an optimized top-down clustering approach with linear prefix trees for scalable frequent pattern discovery in large datasets [PDF]
Scientific ReportsThe extraction of frequent itemsets and association rules is a fundamental challenge in data mining and holds significant importance within the field. Mining techniques utilising Linear Prefix (LP) growth association rules employ a bottom-up methodology ...
M. Sinthuja, M. Diviya, P. Saranya
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A Parallel Apriori Algorithm and FP- Growth Based on SPARK [PDF]
ITM Web of Conferences, 2021 Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets.
Gupta Priyanka, Sawant Vinaya
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Efficient mining of intra-periodic frequent sequences
Array, 2022 Frequent Sequence Mining (FSM) is a fundamental task in data mining. Although FSM algorithms extract frequent patterns, they cannot discover patterns that periodically appear in the data.
Edith Belise Kenmogne +4 more
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Weighted Frequent Itemsets Mining Algorithm Based on Difference Nodeset [PDF]
Jisuanji gongcheng, 2020 To address the low mining efficiency of NFWI,a WN-list based algorithm for weighted frequent itemsets mining,this paper proposes a WDiffNodeset-based weighted frequent itemsets mining algorithm,DiffNFWI.The algorithm extends the data structure of ...
WANG Bin, FANG Xinxiu, WEI Tianyou
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Association Mining for Super Market Sales using UP Growth and Top-K Algorithm [PDF]
ITM Web of Conferences, 2020 Frequent itemsets(HUIs) mining is an evolving field in data mining, that centers around finding itemsets having a utility that meets a user-specified minimum utility by finding all the itemsets.
Bhope Harshal +3 more
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IEEE Access, 2021 One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa.
Wannasiri Thurachon, Worapoj Kreesuradej
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Sliding Window-based Frequent Itemsets Mining over Data Streams using Tail Pointer Table [PDF]
International Journal of Computational Intelligence Systems, 2014 Mining frequent itemsets over transaction data streams is critical for many applications, such as wireless sensor networks, analysis of retail market data, and stock market predication.
Le Wang, Lin Feng, Bo Jin
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On differentially private frequent itemset mining [PDF]
Proceedings of the VLDB Endowment, 2012 We consider differentially private frequent itemset mining. We begin by exploring the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, it leaves a glimmer of hope in that our proof of difficulty relies on the existence of long ...
Chen, Zeng +2 more
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MAXLEN-FI: AN ALGORITHM FOR MINING MAXIMUM- LENGTH FREQUENT ITEMSETS FAST
Tạp chí Khoa học Đại học Đà Lạt, 2018 Association rule mining, one of the most important and well-researched techniques of data mining. Mining frequent itemsets are one of the most fundamental and most time-consuming problems in association rule mining.
Phan Thành Huấn, Lê Hoài Bắc
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