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

^*Bu çalışma için yazışmadan sorumlu yazar

Endüstri Mühendisliği Bölümü

Izmir Bakircay University

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

3 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editörler	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Yayınlayan	Springer Verlag
Sayfalar	160-169
Sayfa sayısı	10
ISBN (Basılı)	9783030237554
DOI'lar	https://doi.org/10.1007/978-3-030-23756-1_21
Yayın durumu	Yayınlandı - 2020
Etkinlik	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Türkiye Süre: 23 Tem 2019 → 25 Tem 2019

Yayın serisi

Adı	Advances in Intelligent Systems and Computing
Hacim	1029
ISSN (Basılı)	2194-5357
ISSN (Elektronik)	2194-5365

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

???event.eventtypes.event.conference???	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Ülke/Bölge	Türkiye
Şehir	Istanbul
Periyot	23/07/19 → 25/07/19

Bibliyografik not

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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; Hac. 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. editör / 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).

@inproceedings{0703ddb16273491d81c2b89ab3c21ede,

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",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "160--169",

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",

}

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, hac. 1029, Springer Verlag, pp. 160-169, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Türkiye, 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; Hac. 1029).

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

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

UR - https://www.scopus.com/pages/publications/85069513514

U2 - 10.1007/978-3-030-23756-1_21

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

M3 - Conference contribution

AN - SCOPUS:85069513514

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 160

EP - 169

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 -

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, editörler, 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

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