Supplier Evaluation Using Fuzzy Clustering

Öztayşi, Başar; Işık, Mine

doi:10.1007/978-3-642-53939-8_4

Cited by 9 publications

(1 citation statement)

References 61 publications

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“…Crisp clustering algorithms match input data to one specific cluster. On the other hand, fuzzy clustering algorithms assign an object to diversified clusters simultaneously with a membership degree (Oztaysi and Isik, 2014). From this point of view, one of the most applied clustering algorithm for fuzzy clustering is FcM clustering that clusters should be determined in advance (Chen et al, 2014).…”

Section: Methodology a Fuzzy C-meansmentioning

confidence: 99%

Outlier Detection in Location Based Systems By Using Fuzzy Clustering

Öztayşi¹,

Onar²,

Kahraman³

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Customer segmentation has been one of most important decision in marketing. In general, demographics of customers, monetary value of customer transactions, types of product/service customers use are the sources of segmentation process. In recent years, new technology enabled new sources of data. On of these new data are the customer location data collected from location based systems (LBS). By using these location data an improved customer insight can be provided to the companies. Segmentation is an important tool for creating customer insight but anomalies in LBS data can prevent a well formed segmentation. In this paper we propose a novel approach to outlier detection in LBS data by using fuzzy c-means algorithm

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Section: Methodology a Fuzzy C-meansmentioning

confidence: 99%