Results 11 to 20 of about 719,228 (260)
Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response [PDF]
The Scientific World Journal, 2014 Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining.
Chongjing Sun +3 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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Towards Rare Itemset Mining [PDF]
19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007), 2007 We 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 ones are of higher interest (e.g., in medical databases, rare combinations of symptoms might provide useful insights for the physicians). Based on an examination of the relevant
Szathmary, Laszlo +2 more
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User’s Constraints in Itemset Mining [PDF]
, 2018 Discovering 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. With a constraint programming approach, we can easily express and efficiently answer queries with user’s constraints on itemsets.
Bessiere, Christian +2 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 interpretability, and a conciseness comparable to closed itemsets. Regular itemsets allow for specifying
RUGGIERI, SALVATORE, Salvatore Ruggieri
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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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Contextual Itemset Mining in DBpedia. [PDF]
, 2014 In this paper we show the potential of contextual itemset mining in the context of Linked Open Data. Contextual itemset mining extracts frequent associations among items considering background information. In the case of Linked Open Data, the background information is represented by an Ontology defined over the data.
Rabatel, Julien +3 more
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Research on association analysis between electricity consumption behaviors and weather factors based on mapreduce [PDF]
Scientific ReportsThe change of weather factors will lead to great changes in users’ electricity consumption behaviors. In order to discover the associations between users’ electricity consumption behavior and weather factors, and meet the needs of efficient mining of ...
Yuehua Yang, Yun Wu
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GMiner: A fast GPU-based frequent itemset mining method for large-scale data
Information Sciences, 2018 Frequent itemset mining is widely used as a fundamental data mining technique. However, as the data size increases, the relatively slow performances of the existing methods hinder its applicability.
Kang-Wook Chon +2 more
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IPHM: Incremental periodic high-utility mining algorithm in dynamic and evolving data environments [PDF]
HeliyonPeriodic high-utility itemset (PHUI) mining can extend beyond the conventional approach of high-utility itemset mining by uncovering recurring customer purchase behaviors common in real-life scenarios (e.g., buying apples and oranges every three days or ...
Huiwu Huang, Shixi Chen, Jiahui Chen
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