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A regression-based algorithm for frequent itemsets mining

A regression-based algorithm for frequent itemsets mining Frequent itemset mining (FIM) is a basic topic in data mining. Most FIM methods build itemset database containing all possible itemsets, and use predefined thresholds to determine whether an itemset is frequent. However, the algorithm has some deficiencies. It is more fit for discrete data rather than ordinal/continuous data, which may result in computational redundancy, and some of the results are difficult to be interpreted. The purpose of this paper is to shed light on this gap by proposing a new data mining method.Design/methodology/approachRegression pattern (RP) model will be introduced, in which the regression model and FIM method will be combined to solve the existing problems. Using a survey data of computer technology and software professional qualification examination, the multiple linear regression model is selected to mine associations between items.FindingsSome interesting associations mined by the proposed algorithm and the results show that the proposed method can be applied in ordinal/continuous data mining area. The experiment of RP model shows that, compared to FIM, the computational redundancy decreased and the results contain more information.Research limitations/implicationsThe proposed algorithm is designed for ordinal/continuous data and is expected to provide inspiration for data stream mining and unstructured data mining.Practical implicationsCompared to FIM, which mines associations between discrete items, RP model could mine associations between ordinal/continuous data sets. Importantly, RP model performs well in saving computational resource and mining meaningful associations.Originality/valueThe proposed algorithms provide a novelty view to define and mine association. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Data Technologies and Applications Emerald Publishing

A regression-based algorithm for frequent itemsets mining

Data Technologies and Applications , Volume 54 (3): 15 – Jul 7, 2020

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References (35)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2514-9288
DOI
10.1108/dta-03-2019-0037
Publisher site
See Article on Publisher Site

Abstract

Frequent itemset mining (FIM) is a basic topic in data mining. Most FIM methods build itemset database containing all possible itemsets, and use predefined thresholds to determine whether an itemset is frequent. However, the algorithm has some deficiencies. It is more fit for discrete data rather than ordinal/continuous data, which may result in computational redundancy, and some of the results are difficult to be interpreted. The purpose of this paper is to shed light on this gap by proposing a new data mining method.Design/methodology/approachRegression pattern (RP) model will be introduced, in which the regression model and FIM method will be combined to solve the existing problems. Using a survey data of computer technology and software professional qualification examination, the multiple linear regression model is selected to mine associations between items.FindingsSome interesting associations mined by the proposed algorithm and the results show that the proposed method can be applied in ordinal/continuous data mining area. The experiment of RP model shows that, compared to FIM, the computational redundancy decreased and the results contain more information.Research limitations/implicationsThe proposed algorithm is designed for ordinal/continuous data and is expected to provide inspiration for data stream mining and unstructured data mining.Practical implicationsCompared to FIM, which mines associations between discrete items, RP model could mine associations between ordinal/continuous data sets. Importantly, RP model performs well in saving computational resource and mining meaningful associations.Originality/valueThe proposed algorithms provide a novelty view to define and mine association.

Journal

Data Technologies and ApplicationsEmerald Publishing

Published: Jul 7, 2020

Keywords: Data mining; Questionnaire; Software industry; Regression; Frequent itemset mining; Ordinal/continuous data

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