Results 151 to 160 of about 2,974 (185)

Mining Frequent Closed Itemsets from Distributed Dataset

2008 International Symposium on Computational Intelligence and Design, 2008
In this paper we address the problem of mining frequent closed itemsets in a highly distributed setting. The extraction of distributed frequent (close) itemsets is an important task in data mining. The paper shows how frequent closed itemsets, mined independently in each site, can be merged in order to derive globally frequent closed itemsets ...
Chunhua Ju, Dongjun Ni
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Mining Closed and Maximal Frequent Itemsets

2011
This chapter contains sections titled: Introduction Preliminaries Existing Approaches for Closed and Maximal Itemset Mining Efficient CFI and MFI Mining: Charm and Genmax Experimental Results Conclusions This chapter contains sections titled: Acknowledgments ...
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An Algorithm for Mining Lower Closed Itemsets

2004
The generalized association rule base(GARB) presented by Li(2003) can efficiently solve the problem of quantity of rule in the process of acquiring rule by traditional association rule mining algorithms. Therefore, how to deduce all rules contained in the rule of GARB becomes an urgent issue in order to support more effective decision-making.
Tian-rui Li, Ming Qing, Jun Ma, Yang Xu
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Mining Frequent Closed Itemsets for Association Rules

2009
Association refers to correlations that exist among data. Association Rule Mining (ARM) is an important data-mining task. It refers to discovery of rules between different sets of attributes/items in very large databases (Agrawal R. & Srikant R. 1994). The discovered rules help in strategic decision making in both commercial and scientific domains.
Anamika Gupta, Shikha Gupta, Naveen Kumar   +2 more
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Frequent Closed Informative Itemset Mining

2007 International Conference on Computational Intelligence and Security (CIS 2007), 2007
Huaiguo Fu, Micheal O. Foghlu, Willie Donnelly   +2 more
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Closed Itemset Mining and Non-redundant Association Rule Mining

2009
DEFINITION Let I be a set of binary-valued attributes, called items. A set X ⊆ I is called an itemset. A transaction database D is a multiset of itemsets, where each itemset, called a transaction, has a unique identifier, called a tid. The support of an itemset X in a dataset D, denoted sup(X), is the fraction of transactions in D where X appears as a ...
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LSCMiner: Efficient Low Support Closed Itemsets Mining

2019
Itemsets with relatively low support values are important since they usually suggest highly confident association rules, which are useful in applications such as recommendation systems and medical data analysis. However, most existing algorithms are mainly designed to mine frequent patterns and thus are time consuming in generating low support patterns.
Yifeng Lu, Florian Richter, Thomas Seidl
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A comparison of two hybrid closed-loop systems in adolescents and young adults with type 1 diabetes (FLAIR): a multicentre, randomised, crossover trial

Lancet, The, 2021
Richard M Bergenstal, Revital Nimri, Roy W Beck   +2 more
exaly  

Mining Frequent Closed Itemsets Without Candidate Generation

2005
Mining frequent closed itemsets provides complete and non-redundant result for the analysis of frequent pattern. Most of the previous studies adopted the FP-tree based conditional FP-tree generation and candidate itemsets generation-and-test approaches.
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Trial of Hybrid Closed-Loop Control in Young Children with Type 1 Diabetes

New England Journal of Medicine, 2023
R Paul Wadwa
exaly