Results 71 to 80 of about 8,356 (188)
arules - A Computational Environment for Mining Association Rules and Frequent Item Sets [PDF]
Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.
Bettina Grün +2 more
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An improvement of FP-Growth association rule mining algorithm based on adjacency table
MATEC Web of Conferences, 2018 FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets.
Yin Ming +3 more
doaj +1 more source
Revisiting Numerical Pattern Mining with Formal Concept Analysis [PDF]
, 2011 In this paper, we investigate the problem of mining numerical data in the framework of Formal Concept Analysis. The usual way is to use a scaling procedure --transforming numerical attributes into binary ones-- leading either to a loss of information or ...
Kaytoue, Mehdi +2 more
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A Fast Approach for Up-Scaling Frequent Itemsets
IEEE Access, 2020 With the rapid growth of data scale and diversification of demand, people have an urgent desire to extract useful frequent itemset from datasets of different scales. It is no doubt that the traditional method can solve the problem.
Runzi Chen, Shuliang Zhao, Mengmeng Liu
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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,
openaire +2 more sources
Frequent Itemset Mining in Big Data With Effective Single Scan Algorithms
IEEE Access, 2018 This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel implementation on ...
Youcef Djenouri +3 more
doaj +1 more source
Characterizing Transactional Databases for Frequent Itemset Mining
, 2019 This paper presents a study of the characteristics of transactional databases used in frequent itemset mining. Such characterizations have typically been used to benchmark and understand the data mining algorithms working on these databases. The aim of our study is to give a picture of how diverse and representative these benchmarking databases are ...
Lezcano Ríos, Christian Gerardo +1 more
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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 a perspective from operations research [PDF]
Many papers on frequent itemsets have been published. Besides somecontests in this field were held. In the majority of the papers the focus ison speed. Ad hoc algorithms and datastructures were introduced.
Kosters, W.A., Pijls, W.H.L.M.
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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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