Outlier detection in location based systems by using fuzzy clustering

Basar Oztaysi; Sezi Cevik Onar; Cengiz Kahraman

Outlier detection in location based systems by using fuzzy clustering

Basar Oztaysi, Sezi Cevik Onar, Cengiz Kahraman

Endüstri Mühendisliği Bölümü

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

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 dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
Editörler	Vilem Novak, Vladimir Marik, Martin Stepnicka, Mirko Navara, Petr Hurtik
Yayınlayan	Atlantis Press
Sayfalar	653-659
Sayfa sayısı	7
ISBN (Elektronik)	9789462527706
Yayın durumu	Yayınlandı - 2020
Etkinlik	11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 - Prague, Czech Republic Süre: 9 Eyl 2019 → 13 Eyl 2019

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
Ülke/Bölge	Czech Republic
Şehir	Prague
Periyot	9/09/19 → 13/09/19

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/).

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Oztaysi, B., Onar, S. C., & Kahraman, C. (2020). Outlier detection in location based systems by using fuzzy clustering. In V. Novak, V. Marik, M. Stepnicka, M. Navara, & P. Hurtik (Eds.), Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 (pp. 653-659). (Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019). Atlantis Press.

Oztaysi, Basar ; Onar, Sezi Cevik ; Kahraman, Cengiz. / Outlier detection in location based systems by using fuzzy clustering. Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. editör / Vilem Novak ; Vladimir Marik ; Martin Stepnicka ; Mirko Navara ; Petr Hurtik. Atlantis Press, 2020. pp. 653-659 (Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019).

@inproceedings{920a985b9aca41088b36a6cbe900a4cc,

title = "Outlier detection in location based systems by using fuzzy clustering",

abstract = "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.",

keywords = "Fuzzy c-Means, Fuzzy clustering, Location Based Systems, Outlier detection, Segmentation",

author = "Basar Oztaysi and Onar, {Sezi Cevik} and Cengiz Kahraman",

note = "Publisher Copyright: Copyright {\textcopyright} 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/).; 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 ; Conference date: 09-09-2019 Through 13-09-2019",

year = "2020",

language = "English",

series = "Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019",

publisher = "Atlantis Press",

pages = "653--659",

editor = "Vilem Novak and Vladimir Marik and Martin Stepnicka and Mirko Navara and Petr Hurtik",

booktitle = "Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019",

}

Oztaysi, B , Onar, SC & Kahraman, C 2020, Outlier detection in location based systems by using fuzzy clustering. in V Novak, V Marik, M Stepnicka, M Navara & P Hurtik (eds), Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019, Atlantis Press, pp. 653-659, 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019, Prague, Czech Republic, 9/09/19.

Outlier detection in location based systems by using fuzzy clustering. / Oztaysi, Basar ; Onar, Sezi Cevik ; Kahraman, Cengiz.
Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. ed. / Vilem Novak; Vladimir Marik; Martin Stepnicka; Mirko Navara; Petr Hurtik. Atlantis Press, 2020. p. 653-659 (Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Outlier detection in location based systems by using fuzzy clustering

AU - Oztaysi, Basar

AU - Onar, Sezi Cevik

AU - Kahraman, Cengiz

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Fuzzy c-Means

KW - Fuzzy clustering

KW - Location Based Systems

KW - Outlier detection

KW - Segmentation

UR - http://www.scopus.com/inward/record.url?scp=85090902671&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85090902671

T3 - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019

SP - 653

EP - 659

BT - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019

A2 - Novak, Vilem

A2 - Marik, Vladimir

A2 - Stepnicka, Martin

A2 - Navara, Mirko

A2 - Hurtik, Petr

PB - Atlantis Press

T2 - 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019

Y2 - 9 September 2019 through 13 September 2019

ER -

Oztaysi B , Onar SC , Kahraman C. Outlier detection in location based systems by using fuzzy clustering. In Novak V, Marik V, Stepnicka M, Navara M, Hurtik P, editörler, Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. Atlantis Press. 2020. p. 653-659. (Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019).

Outlier detection in location based systems by using fuzzy clustering

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Bibliyografik not

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