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

Department of Industrial Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
Editors	Vilem Novak, Vladimir Marik, Martin Stepnicka, Mirko Navara, Petr Hurtik
Publisher	Atlantis Press
Pages	653-659
Number of pages	7
ISBN (Electronic)	9789462527706
Publication status	Published - 2020
Event	11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 - Prague, Czech Republic Duration: 9 Sept 2019 → 13 Sept 2019

Publication series

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

Conference

Conference	11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
Country/Territory	Czech Republic
City	Prague
Period	9/09/19 → 13/09/19

Bibliographical note

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/).

Keywords

Fuzzy c-Means
Fuzzy clustering
Location Based Systems
Outlier detection
Segmentation

Cite this

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. editor / 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).

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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",

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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",

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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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Onar, Sezi Cevik

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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

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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, editors, 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

Abstract

Publication series

Conference

Bibliographical note

Keywords

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