Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering

Onur Dogan; Basar Oztaysi

doi:10.1007/978-3-030-23756-1_21

Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering

Onur Dogan^*, Basar Oztaysi

^*Corresponding author for this work

Department of Industrial Engineering

Izmir Bakircay University

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

3 Citations (Scopus)

Abstract

Customer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths.

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	160-169
Number of pages	10
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_21
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

Fuzzy C-Medoids
Gender prediction
Indoor paths
Levenshtein distances
Path classification

Access to Document

10.1007/978-3-030-23756-1_21

Cite this

Dogan, O., & Oztaysi, B. (2020). Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering. 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. 160-169). (Advances in Intelligent Systems and Computing; Vol. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_21

Dogan, Onur ; Oztaysi, Basar. / Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering. 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. 160-169 (Advances in Intelligent Systems and Computing).

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title = "Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering",

abstract = "Customer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths.",

keywords = "Fuzzy C-Medoids, Gender prediction, Indoor paths, Levenshtein distances, Path classification",

author = "Onur Dogan 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_21",

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Dogan, O & Oztaysi, B 2020, Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering. 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. 160-169, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_21

Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering. / Dogan, Onur; 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. 160-169 (Advances in Intelligent Systems and Computing; Vol. 1029).

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

TY - GEN

T1 - Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering

AU - Dogan, Onur

AU - Oztaysi, Basar

PY - 2020

Y1 - 2020

N2 - Customer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths.

AB - Customer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths.

KW - Fuzzy C-Medoids

KW - Gender prediction

KW - Indoor paths

KW - Levenshtein distances

KW - Path classification

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SN - 9783030237554

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

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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 -

Dogan O, Oztaysi B. Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering. 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. 160-169. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_21

Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering

Abstract

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Bibliographical note

Keywords

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