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A MapReduce Approach to Model Big Data with Fuzzy Functions Identified Based on Fuzzy C-Means Algorithm.

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Title: A MapReduce Approach to Model Big Data with Fuzzy Functions Identified Based on Fuzzy C-Means Algorithm.
Authors: ARTUT, Ahmet1,2 aartut@cumhuriyet.edu.tr, GÖLEÇ, Adem3 ademgolec@erciyes.edu.tr
Source: Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 1, p294-304. 11p.
Subjects: Big data, Data modeling, Parallel processing, Fuzzy clustering technique, Computer performance, Fuzzy logic, Electronic data processing, Parallel programming
Abstract: Recently, big data has become increasingly important in the fields of scientific research and application. However, due to the characteristic features of big data such as high volume, velocity, variety, variability, value, and complexity, processing it with traditional analysis methods is quite a challenging process. In this context, frameworks like MapReduce are commonly used in the modeling of big data and in parallel and distributed data processing techniques. In this study, it is aimed to use fuzzy functions based on the fuzzy c-means (FCM) algorithm under the MapReduce architecture for modeling systems based on large data sets. In the study, it is explained in detail how the FCM algorithm is parallelized in the mapping phase; subsequently, it is demonstrated how the data is reduced in the reduce phase and how the fuzzy functions are derived. The proposed approach demonstrates the effectiveness of fuzzy functions within the MapReduce framework in modeling systems based on various large datasets. Additionally, the success of the methodology has been thoroughly discussed through the evaluation of the obtained fuzzy functions and performance analysis. [ABSTRACT FROM AUTHOR]
Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: A MapReduce Approach to Model Big Data with Fuzzy Functions Identified Based on Fuzzy C-Means Algorithm.
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  Data: <searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+clustering+technique%22">Fuzzy clustering technique</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+performance%22">Computer performance</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink>
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  Data: Recently, big data has become increasingly important in the fields of scientific research and application. However, due to the characteristic features of big data such as high volume, velocity, variety, variability, value, and complexity, processing it with traditional analysis methods is quite a challenging process. In this context, frameworks like MapReduce are commonly used in the modeling of big data and in parallel and distributed data processing techniques. In this study, it is aimed to use fuzzy functions based on the fuzzy c-means (FCM) algorithm under the MapReduce architecture for modeling systems based on large data sets. In the study, it is explained in detail how the FCM algorithm is parallelized in the mapping phase; subsequently, it is demonstrated how the data is reduced in the reduce phase and how the fuzzy functions are derived. The proposed approach demonstrates the effectiveness of fuzzy functions within the MapReduce framework in modeling systems based on various large datasets. Additionally, the success of the methodology has been thoroughly discussed through the evaluation of the obtained fuzzy functions and performance analysis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.17559/TV-20250202002322
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        Text: English
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        PageCount: 11
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      – SubjectFull: Big data
        Type: general
      – SubjectFull: Data modeling
        Type: general
      – SubjectFull: Parallel processing
        Type: general
      – SubjectFull: Fuzzy clustering technique
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      – SubjectFull: Computer performance
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      – SubjectFull: Fuzzy logic
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      – SubjectFull: Electronic data processing
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      – SubjectFull: Parallel programming
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            – D: 01
              M: 01
              Text: 2026
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