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Finding the True Frequent Itemsets [PDF]
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014 Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction $θ$ of a transactional dataset $\mathcal{D}$. Often though, the ultimate goal of mining $\mathcal{D}$ is not an analysis of the dataset \emph{per se}, but the understanding of the underlying process that ...
Riondato, Matteo, VANDIN, FABIO
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Mining Frequent Itemsets in a Stream [PDF]
Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007 We study the problem of finding frequent itemsets in a continuous stream of transactions. The current frequency of an itemset in a stream is defined as its maximal frequency over all possible windows in the stream from any point in the past until the current state that satisfy a minimal length constraint.
Toon Calders +2 more
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TKFIM: Top-K frequent itemset mining technique based on equivalence classes [PDF]
PeerJ Computer Science, 2021 Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially.
Saood Iqbal +5 more
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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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Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases
IEEE Access, 2023 Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database.
Hong N. Dao +5 more
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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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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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An algebraic semigroup method for discovering maximal frequent itemsets
Open Mathematics, 2022 Discovering maximal frequent itemsets is an important issue and key technique in many data mining problems such as association rule mining. In the literature, generating maximal frequent itemsets proves either to be NP-hard or to have O(l34l(m+n))O\left({
Liu Jiang +5 more
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High Scalability Document Clustering Algorithm Based On Top-K Weighted Closed Frequent Itemsets
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2021 Documents clustering based on frequent itemsets can be regarded a new method of documents clustering which is aimed to overcome curse of dimensionality of items produced by documents being clustered.
Gede Aditra Pradnyana, Arif Djunaidy
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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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