Results 11 to 20 of about 8,833 (199)
An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm
Complexity, 2022 Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent ...
Hussein A. Alsaeedi, Ahmed S. Alhegami
doaj +1 more source
Proposed Algorithm for Extracting Association Rule Depend on Closed Frequent Itemset (EACFI) [PDF]
Engineering and Technology Journal, 2011 Association rules are important one of data mining activities. All algorithms of association rule mining consist of finding frequency of itemsets, which satisfy a minimum support threshold, and then compute confidence percentage for each k-itemsets to ...
Emad k. Jbbar, Yaser Munther
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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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Memory-efficient frequent-itemset mining
Proceedings of the 14th International Conference on Extending Database Technology, 2011 Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms---which operate entirely in main memory and avoid expensive disk accesses---and in particular the prefix tree-based algorithm FP-growth are generally among the most efficient of the available algorithms.
Schlegel, Benjamin +2 more
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Mining All Non-derivable Frequent Itemsets [PDF]
, 2002 3 ...
Calders, T., GOETHALS, Bart
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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.
Calders, Toon +2 more
openaire +2 more sources
On the Complexity of Mining Itemsets from the Crowd Using Taxonomies [PDF]
, 2013 We study the problem of frequent itemset mining in domains where data is not recorded in a conventional database but only exists in human knowledge. We provide examples of such scenarios, and present a crowdsourcing model for them.
Amarilli, Antoine +2 more
core +2 more sources
A Bitmap Approach for Mining Erasable Itemsets
IEEE Access, 2021 Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of ...
Tzung-Pei Hong +4 more
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Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems [PDF]
, 2018 Frequent itemset mining leads to the discovery of associations and correlations among items in large transactional databases. Apriori is a classical frequent itemset mining algorithm, which employs iterative passes over database combining with generation
Zymbler, Mikhail
core +3 more sources
Earth Surface Processes and Landforms, Volume 48, Issue 11, Page 2141-2151, 15 September 2023., 2023 I argue that a concise [decimal latitude,longitude] tuple form of geo‐referencing be used to identify landforms and sample sites, in data strings and image metadata. This form enables searching and information exchange from geomorphology into and beyond Critical Zone studies via the FAIR data principles: findability, accessibility, interoperability and
W. Brian Whalley
wiley +1 more source