Outlier detection in location based systems by using fuzzy clustering

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Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
EditörlerVilem Novak, Vladimir Marik, Martin Stepnicka, Mirko Navara, Petr Hurtik
YayınlayanAtlantis Press
Sayfalar653-659
Sayfa sayısı7
ISBN (Elektronik)9789462527706
Yayın durumuYayınlandı - 2020
Etkinlik11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 - Prague, Czech Republic
Süre: 9 Eyl 201913 Eyl 2019

Yayın serisi

AdıProceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019

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???event.eventtypes.event.conference???11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
Ülke/BölgeCzech Republic
ŞehirPrague
Periyot9/09/1913/09/19

Bibliyografik not

Publisher Copyright:
Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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