Results 21 to 30 of about 10,877 (201)
FRI-miner: fuzzy rare itemset mining [PDF]
Applied Intelligence, 2021 Preprint.
Cui, Yanling +3 more
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Efficient chain structure for high-utility sequential pattern mining [PDF]
, 2020 High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases.
Djenouri, Youcef +4 more
core +4 more sources
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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A Reinduction-Based Approach for Efficient High Utility Itemset Mining from Incremental Datasets
Data Science and Engineering, 2023 High utility itemset mining is a crucial research area that focuses on identifying combinations of itemsets from databases that possess a utility value higher than a user-specified threshold.
Pushp Sra, Satish Chand
doaj +1 more source
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
EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data
International Journal of Information Management Data Insights, 2022 High utility itemset mining (HUIM) is a data mining technique that identifies the itemsets with utility levels exceeding a pre-determined threshold. The factor utility is described as the combination of magnitude and element of significance for an item ...
Vandna Dahiya, Sandeep Dalal
doaj +1 more source
An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets [PDF]
, 2009 As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications.
Kirsch, Adam +5 more
core +3 more sources
Hybrid Recommendation System Memanfaatkan Penggalian Frequent Itemset dan Perbandingan Keyword
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2015 Abstrak Recommendation system sering dibangun dengan memanfaatkan data peringkat item dan data identitas pengguna. Data peringkat item merupakan data yang langka pada sistem yang baru dibangun.
Wayan Gede Suka Parwita, Edi Winarko
doaj +1 more source
Mining data quality rules based on T-dependence [PDF]
, 2019 Since their introduction in 1976, edit rules have been a standard tool in statistical analysis. Basically, edit rules are a compact representation of non-permitted combinations of values in a dataset.
Boeckling, Toon +2 more
core +1 more source
Non-derivable itemset mining [PDF]
Data Mining and Knowledge Discovery, 2007 All frequent itemset mining algorithms rely heavily on the monotonicity principle for pruning. This principle allows for excluding candidate itemsets from the expensive counting phase. In this paper, we present sound and complete deduction rules to derive bounds on the support of an itemset.
Calders, Toon, Goethals, Bart
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