Results 81 to 90 of about 1,488,653 (205)
A partition enhanced mining algorithm for distributed association rule mining systems
Egyptian Informatics Journal, 2015 The extraction of patterns and rules from large distributed databases through existing Distributed Association Rule Mining (DARM) systems is still faced with enormous challenges such as high response times, high communication costs and inability to adapt
A.O. Ogunde, O. Folorunso, A.S. Sodiya
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Mining frequent closed itemsets from distributed repositories
, 2007 In this paper we address the problem of mining frequent closed itemsets in a highly distributed setting like a Grid. The extraction of frequent (closed) itemsets is an important problem in Data Mining, and is a very expensive phase needed to extract from
Lucchese C +3 more
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FREQUENT ITEMSETS MINING FOR BIG DATA
, 2019 Frequent Itemsets Mining (FIM) is a fundamental mining model and plays an important role in Data Mining. It has a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Generally speaking, FIM counts the frequencies of co-occurrence items,
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PLoS ONE, 2015 Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples.
Ujjwal Maulik +3 more
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Mining Frequent Itemsets with Category-Based Constraints
, 2003 The discovery of frequent itemsets is a fundamental task of association rule mining. The challenge is the computational complexity of the itemset search space. One of the solutions for this is to use constraints to focus on some specific itemsets.
Hui, Siu Cheung +5 more
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Mining of Global Maximum Frequent Itemsets Based on FP-Tree
, 2011 As far as we know, a little research of mining global maximum frequent itemsets has been done. The paper proposed an algorithm for mining global maximum frequent itemsets based on FP-tree, namely, AMGMFI algorithm.
Bo He
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An efficient approach for interactive mining of frequent itemsets
, 2005 There have been many studies on efficient discovery of frequent itemsets in large databases. However, it is nontrivial to mine frequent itemsets under interactive circumstances where users often change minimum support threshold (minsup) because the ...
Xin Li +8 more
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Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data
IEEE AccessFrequent itemset mining (FIM) is a highly resource-demanding data-mining task fundamental to numerous data-mining applications. Support calculation is a frequently performed computation-intensive operation of FIM algorithms, whereas storing transactional
Muhammad Sadeequllah +3 more
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Constraint Programming for Mining Borders of Frequent Itemsets
, 2019 International audienceFrequent itemset mining is one of the most studied tasks in knowledge discovery. It is often reduced to mining the positive border of frequent itemsets, i.e. maximal frequent itemsets.
Christian Bessiere +5 more
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Discovery of Frequent Itemsets: Frequent Item Tree-Based Approach
Journal of ICT Research and Applications, 2013 Mining frequent patterns in large transactional databases is a highly researched area in the field of data mining. Existing frequent pattern discovering algorithms suffer from many problems regarding the high memory dependency when mining large amount of
A. V. Senthil Kumar, R. S. D. Wahidabanu
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