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WIREs Data Mining Knowl Discov
Impact Factor: 2.111

Data mining and machine learning in textile industry

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Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K‐means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions.

Main areas related to data mining in textile industry.
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Number of publications related to classification and clustering in textile sector in Elsevier's Scopus by year.
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An example decision tree related to textile industry.
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Types of neural networks commonly used in the textile industry.
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Neural network application examples related to textile industry; (a) production planning, (b) predicting utility properties; and (c) performance development.
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Classification and clustering algorithms commonly used in textile industry.
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Application Areas > Business and Industry
Application Areas > Industry Specific Applications
Application Areas > Science and Technology

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