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Data Mining for Multicriteria Single Facility Location Problems

Data Mining for Multicriteria Single Facility Location Problems

Seda Tolun, Halit Alper Tayalı
Copyright: © 2016 |Pages: 24
ISBN13: 9781522500759|ISBN10: 1522500758|EISBN13: 9781522500766
DOI: 10.4018/978-1-5225-0075-9.ch007
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MLA

Tolun, Seda, and Halit Alper Tayalı. "Data Mining for Multicriteria Single Facility Location Problems." Intelligent Techniques for Data Analysis in Diverse Settings, edited by Numan Celebi, IGI Global, 2016, pp. 147-170. https://doi.org/10.4018/978-1-5225-0075-9.ch007

APA

Tolun, S. & Tayalı, H. A. (2016). Data Mining for Multicriteria Single Facility Location Problems. In N. Celebi (Ed.), Intelligent Techniques for Data Analysis in Diverse Settings (pp. 147-170). IGI Global. https://doi.org/10.4018/978-1-5225-0075-9.ch007

Chicago

Tolun, Seda, and Halit Alper Tayalı. "Data Mining for Multicriteria Single Facility Location Problems." In Intelligent Techniques for Data Analysis in Diverse Settings, edited by Numan Celebi, 147-170. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0075-9.ch007

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Abstract

This chapter focuses on available data analysis and data mining techniques to find the optimal location of the Multicriteria Single Facility Location Problem (MSFLP) at diverse business settings. Solving for the optimal of an MSFLP, there exists numerous multicriteria decision analysis techniques. Mainstream models are mentioned in this chapter, while presenting a general classification of the MSFLP and its framework. Besides, topics from machine learning with respect to decision analysis are covered: Unsupervised Principal Components Analysis ranking (PCA-rank) and supervised Support Vector Machines ranking (SVM-rank). This chapter proposes a data mining perspective for the multicriteria single facility location problem and proposes a new approach to the facility location problem with the combination of the PCA-rank and ranking SVMs.

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