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The Cluster Analysis In The Manufacturing Industry With K-Means Method: An Application For Turkey

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  • Zeynep Karaca

    (Erzurum Technical University, Turkey)

Abstract

Clusters are networks of firms and manufacturers that are strongly connected within the production chain, creating value added together. There are some demand and supply-side advantages of being in a cluster, including proximity to the consumer, reduction in the cost of finding customers, reputation, information externality, output competition, knowledge distribution, an expert workforce, and infrastructural utility. With clusters, employees can be prevented from being employed in different places and skilled workers can be attracted. In addition, clustering facilitates access to key inputs and reduces transaction costs through the use of local suppliers, meaning that all market, technology and competitive information accumulates in the cluster. This study investigates data mining in Turkey using cluster analysis techniques to determine how the manufacturing of wood products industry sectors are clustered among the regions of Nace Rev. This is done using the number of local units and the number of employees in the manufacturing industry sectors according to two classifications. Data obtained from this study will be used in the Annual Industry and Service Statistics published by Turkey's Statistics Institute. Since the most recently published data was provided in 2015, the number of local units and employment figures for 2015 will be used in the study. The algorithm for the k-means method used in this study was written using Microsoft Excel.

Suggested Citation

  • Zeynep Karaca, 2018. "The Cluster Analysis In The Manufacturing Industry With K-Means Method: An Application For Turkey," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 6(3), pages 1-12.
    • Handle: RePEc:ejn:ejefjr:v:6:y:2018:i:3:p:1-12
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    References listed on IDEAS

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    Keywords

    Cluster; K-Means; Algorithm;
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