A data mining algorithm for fuzzy transaction data

Chen, Chin-Yuan; Liang, Gin-Shuh; Su, Yuh-Ling; Liao, Mao-Sheng

doi:10.1007/s11135-013-9934-1

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Our work differs from such publications with much originality. However, recent papers by Oleinik (2022), Sinclair-Desgagné (2021), Franzosi (2021), Rawat and Sood (2021), Chen et al (2014) may be mentioned as being of interest to readers for increasing the relevance size of the bibliography toward connected domains.…”

Section: C) Feature Selectionmentioning

confidence: 99%

Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods

et al. 2022

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Highly cited papers are influenced by external factors that are not directly related to the document's intrinsic quality. In this study, 50 characteristics for measuring the performance of 68 highly cited papers, from the Journal of The American Medical Informatics Association indexed in Web of Science (WOS), from 2009 to 2019 were investigated. In the first step, a Pearson correlation analysis is performed to eliminate variables with zero or weak correlation with the target (“dependent”) variable (number of citations in WOS). Consequently, 32 variables are selected for the next step. By applying the Ridge technique, 13 features show a positive effect on the number of citations. Using three different algorithms, i.e., Ridge, Lasso, and Boruta, 6 factors appear to be the most relevant ones. The "Number of citations by international researchers", "Journal self-citations in citing documents”, and "Authors' self-citations in citing documents”, are recognized as the most important features by all three methods here used. The "First author's scientific age”, "Open-access paper”, and "Number of first author's citations in WOS" are identified as the important features of highly cited papers by only two methods, Ridge and Lasso. Notice that we use specific machine learning algorithms as feature selection methods (Ridge, Lasso, and Boruta) to identify the most important features of highly cited papers, tools that had not previously been used for this purpose. In conclusion, we re-emphasize the performance resulting from such algorithms. Moreover, we do not advise authors to seek to increase the citations of their articles by manipulating the identified performance features. Indeed, ethical rules regarding these characteristics must be strictly obeyed.

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Section: C) Feature Selectionmentioning

confidence: 99%

Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods

et al. 2022

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“…Data mining is an old activity that, through the act of analyzing data, was performed even before the invention of computers (Linoff and Berry, 2011). This action refers to the general process of discovering useful knowledge from existing data (Fayyad et al , 1996a) available in large quantities (Chen et al , 2014). It can also be conceptualized as the process of finding interesting structure in data (Roiger, 2017).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Algorithm applied: attracting MSEs to business associations

Moraes

Schaefer

Schreiber

et al. 2020

JBIM

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Purpose This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members. Design/methodology/approach As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation. Findings The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association. Research limitations/implications This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus. Practical implications The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA. Social implications Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs. Originality/value This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way.

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“…Nowadays, researchers were put forward a number data mining algorithms. For example, Data mining algorithm based on fuzzy sets [1][2]， data mining algorithm based on clustering algorithm [3][4], Data mining algorithms based on neural networks [5][6], etc. However, we can found that these algorithms still have lot problems in practical.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Based on Compensation Fuzzy Neural Networks

Qiu¹

2015

Advances in Social Science, Education and Humanities Research

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The purpose: Solve the existing problems of fuzzy neural network are processing speed and low accuracy. Methods: This paper presents a data mining based on compensation fuzzy neural networks. Through research and analysis of the traditional compensation neural network, we are optimized it according to the characteristics of data mining. And optimize the calculation and training of the data mining. The result: In the first group can be seen that using this algorithm can be get a higher convergence. In the case of less data, the accuracy of the proposed algorithm is almost the same with literature algorithm. In the case of a large amount of data, the proposed method is better than other algorithms. In conclusion: The experimental results basically consistent with the expected results. It can maintain convergence effect, while ensuring the accuracy of the network.

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A data mining algorithm for fuzzy transaction data

Cited by 3 publications

References 24 publications

Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods

Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods

Algorithm applied: attracting MSEs to business associations

Data Mining Based on Compensation Fuzzy Neural Networks

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