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WIREs Data Mining Knowl Discov
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Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

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The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction
Summary of the five most cited papers per year in the periods 2007–2011 (first period) and 2012–2017 (second period) by data‐mining problem type
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Relationships among the most relevant ISI categories for evolutionary data mining (EDM) publications
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(a) Percentages of the number of publications separated by evolutionary algorithm (EA) (Q1) and emerging‐evolutionary computation (EC)‐based (EC‐EA, that is, Q2−Q1); (b) Percentages of citations separated by EA (Q1) and emerging‐EC‐based (EC‐EA, that is, Q2−Q1) percentage of the number of publications and citations in the evolutionary data mining (EDM) area within the top 10% of papers in Computer Science from 2007 to 2017
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Report produced in June 16, 2017; total number of publications on evolutionary data mining (EDM): 1,959; sum of times cited: 57,905; average citations per item: 29.56
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Technologies > Prediction
Technologies > Classification
Technologies > Computational Intelligence
Fundamental Concepts of Data and Knowledge > Knowledge Representation

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