Results 11 to 20 of about 659,904 (212)
WIREs Data Mining and Knowledge Discovery, 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
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Frequent regular itemset mining [PDF]
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010 Concise representations of frequent itemsets sacrifice readability and direct interpretability by a data analyst of the concise patterns extracted. In this paper, we introduce an extension of itemsets, called regular, with an immediate semantics and ...
RUGGIERI, SALVATORE, Salvatore Ruggieri
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Contextual Itemset Mining in DBpedia [PDF]
, 2014 [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-SISOLD4KD'2014: 1st Workshop on Linked Data for Knowledge Discovery with ECML PKDD'2014: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
Ienco, Dino +7 more
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Towards Rare Itemset Mining [PDF]
19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007), 2007 site de la conférence : http://ictai07.ceid.upatras.gr/International audienceWe describe here a general approach for rare itemset mining. While mining literature has been almost exclusively focused on frequent itemsets, in many practical situations rare ...
Napoli, Amedeo +5 more
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User's Constraints in Itemset Mining [PDF]
, 2018 International audienceDiscovering significant itemsets is one of the fundamental tasks in data mining. It has recently been shown that constraint programming is a flexible way to tackle data mining tasks.
Christian Bessiere +5 more
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2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016 International audienceWe introduce a preferences-based itemset mining framework. Preferences are encoded by a penalty function over the transactions in a database. We define an itemset mining problem where we associate to each transaction a penalty value.
Kaci, Souhila +7 more
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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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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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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
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Efficient Associate Rules Mining Based on Topology for Items of Transactional Data
Mathematics, 2023 A challenge in association rules’ mining is effectively reducing the time and space complexity in association rules mining with predefined minimum support and confidence thresholds from huge transaction databases.
Bo Li, Zheng Pei, Chao Zhang, Fei Hao
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