A fuzzy base classifier for fuzzy data included location segmentation

Sultan Ceren Oner; Basar Oztaysi

doi:10.1007/978-3-030-23756-1_22

A fuzzy base classifier for fuzzy data included location segmentation

Sultan Ceren Oner^*, Basar Oztaysi

^*Corresponding author for this work

Department of Industrial Engineering

Istanbul Technical University

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

Abstract

Location Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely adapted to understand consumer location visiting tendency. In this study, we propose a strategy of analysis, by combining Adaboost algorithm and the fuzzy decision tree methodology for fuzzy data. The results emphasis on the heterogeneity in consumers’ place preferences and implications for marketing are offered.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Publisher	Springer Verlag
Pages	170-177
Number of pages	8
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_22
Publication status	Published - 2020
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Duration: 23 Jul 2019 → 25 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1029
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Country/Territory	Turkey
City	Istanbul
Period	23/07/19 → 25/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Base classifier
Location segmentation
Micro segmentation

Access to Document

10.1007/978-3-030-23756-1_22

Cite this

Oner, S. C., & Oztaysi, B. (2020). A fuzzy base classifier for fuzzy data included location segmentation. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 170-177). (Advances in Intelligent Systems and Computing; Vol. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_22

Oner, Sultan Ceren ; Oztaysi, Basar. / A fuzzy base classifier for fuzzy data included location segmentation. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 170-177 (Advances in Intelligent Systems and Computing).

@inproceedings{7c163171107045f994c6eab77503bbab,

title = "A fuzzy base classifier for fuzzy data included location segmentation",

abstract = "Location Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely adapted to understand consumer location visiting tendency. In this study, we propose a strategy of analysis, by combining Adaboost algorithm and the fuzzy decision tree methodology for fuzzy data. The results emphasis on the heterogeneity in consumers{\textquoteright} place preferences and implications for marketing are offered.",

keywords = "Base classifier, Location segmentation, Micro segmentation",

author = "Oner, {Sultan Ceren} and Basar Oztaysi",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

year = "2020",

doi = "10.1007/978-3-030-23756-1_22",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "170--177",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and A.Cagri Tolga",

booktitle = "Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference",

address = "Germany",

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Oner, SC & Oztaysi, B 2020, A fuzzy base classifier for fuzzy data included location segmentation. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, vol. 1029, Springer Verlag, pp. 170-177, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_22

A fuzzy base classifier for fuzzy data included location segmentation. / Oner, Sultan Ceren; Oztaysi, Basar.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 170-177 (Advances in Intelligent Systems and Computing; Vol. 1029).

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

TY - GEN

T1 - A fuzzy base classifier for fuzzy data included location segmentation

AU - Oner, Sultan Ceren

AU - Oztaysi, Basar

PY - 2020

Y1 - 2020

N2 - Location Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely adapted to understand consumer location visiting tendency. In this study, we propose a strategy of analysis, by combining Adaboost algorithm and the fuzzy decision tree methodology for fuzzy data. The results emphasis on the heterogeneity in consumers’ place preferences and implications for marketing are offered.

AB - Location Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely adapted to understand consumer location visiting tendency. In this study, we propose a strategy of analysis, by combining Adaboost algorithm and the fuzzy decision tree methodology for fuzzy data. The results emphasis on the heterogeneity in consumers’ place preferences and implications for marketing are offered.

KW - Base classifier

KW - Location segmentation

KW - Micro segmentation

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U2 - 10.1007/978-3-030-23756-1_22

DO - 10.1007/978-3-030-23756-1_22

M3 - Conference contribution

AN - SCOPUS:85069527384

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 170

EP - 177

BT - Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2019

Y2 - 23 July 2019 through 25 July 2019

ER -

Oner SC, Oztaysi B. A fuzzy base classifier for fuzzy data included location segmentation. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 170-177. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_22

A fuzzy base classifier for fuzzy data included location segmentation

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Keywords

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