Fuzzy Clustering Based Association Rule Mining: A Case Study on Ecommerce

Başar Öztayşi; Pelin Yurdadön; Sezi Çevik Onar

doi:10.1007/978-3-031-09173-5_15

Fuzzy Clustering Based Association Rule Mining: A Case Study on Ecommerce

Başar Öztayşi^*, Pelin Yurdadön, Sezi Çevik Onar

^*Corresponding author for this work

Department of Industrial Engineering

Modanisa Head Office

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

1 Citation (Scopus)

Abstract

Association rule mining (ARM) refers to a procedure that focuses on finding frequent patterns in various data sources. The most commonly used area is the retail sales data and rules which show an association between sales of two products are investigated. To this end, sales data is preprocessed, and algorithms are used to find the association rules by using the specific threshold values of Support and Confidence parameters. In this study, we investigated the effects of using fuzzy clustering with association rule mining. A case study from the E-commerce area is selected and sales data for a specific period is analyzed. First ARM is applied to the whole data, then the data is segmented by using Fuzzy Clustering, and ARM is applied to all segments. Later the resulting rules are compared and the effects of segmentation on ARM results are analyzed.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, A. Cagri Tolga, Selcuk Cebi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	112-118
Number of pages	7
ISBN (Print)	9783031091728
DOIs	https://doi.org/10.1007/978-3-031-09173-5_15
Publication status	Published - 2022
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2022 - Izmir, Turkey Duration: 19 Jul 2022 → 21 Jul 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	504 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2022
Country/Territory	Turkey
City	Izmir
Period	19/07/22 → 21/07/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Association rule mining
E-Commerce
Fuzzy clustering

Access to Document

10.1007/978-3-031-09173-5_15

Cite this

Öztayşi, B., Yurdadön, P., & Onar, S. Ç. (2022). Fuzzy Clustering Based Association Rule Mining: A Case Study on Ecommerce. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, A. C. Tolga, & S. Cebi (Eds.), Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference (pp. 112-118). (Lecture Notes in Networks and Systems; Vol. 504 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-09173-5_15

Öztayşi, Başar ; Yurdadön, Pelin ; Onar, Sezi Çevik. / Fuzzy Clustering Based Association Rule Mining : A Case Study on Ecommerce. Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; A. Cagri Tolga ; Selcuk Cebi. Springer Science and Business Media Deutschland GmbH, 2022. pp. 112-118 (Lecture Notes in Networks and Systems).

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abstract = "Association rule mining (ARM) refers to a procedure that focuses on finding frequent patterns in various data sources. The most commonly used area is the retail sales data and rules which show an association between sales of two products are investigated. To this end, sales data is preprocessed, and algorithms are used to find the association rules by using the specific threshold values of Support and Confidence parameters. In this study, we investigated the effects of using fuzzy clustering with association rule mining. A case study from the E-commerce area is selected and sales data for a specific period is analyzed. First ARM is applied to the whole data, then the data is segmented by using Fuzzy Clustering, and ARM is applied to all segments. Later the resulting rules are compared and the effects of segmentation on ARM results are analyzed.",

keywords = "Association rule mining, E-Commerce, Fuzzy clustering",

author = "Ba{\c s}ar {\"O}ztay{\c s}i and Pelin Yurdad{\"o}n and Onar, {Sezi {\c C}evik}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2022 ; Conference date: 19-07-2022 Through 21-07-2022",

year = "2022",

doi = "10.1007/978-3-031-09173-5_15",

language = "English",

isbn = "9783031091728",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "112--118",

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

booktitle = "Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference",

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Öztayşi, B, Yurdadön, P & Onar, SÇ 2022, Fuzzy Clustering Based Association Rule Mining: A Case Study on Ecommerce. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, AC Tolga & S Cebi (eds), Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference. Lecture Notes in Networks and Systems, vol. 504 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 112-118, International Conference on Intelligent and Fuzzy Systems, INFUS 2022, Izmir, Turkey, 19/07/22. https://doi.org/10.1007/978-3-031-09173-5_15

Fuzzy Clustering Based Association Rule Mining: A Case Study on Ecommerce. / Öztayşi, Başar; Yurdadön, Pelin; Onar, Sezi Çevik.
Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; A. Cagri Tolga; Selcuk Cebi. Springer Science and Business Media Deutschland GmbH, 2022. p. 112-118 (Lecture Notes in Networks and Systems; Vol. 504 LNNS).

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

TY - GEN

T1 - Fuzzy Clustering Based Association Rule Mining

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2022

AU - Öztayşi, Başar

AU - Yurdadön, Pelin

AU - Onar, Sezi Çevik

PY - 2022

Y1 - 2022

N2 - Association rule mining (ARM) refers to a procedure that focuses on finding frequent patterns in various data sources. The most commonly used area is the retail sales data and rules which show an association between sales of two products are investigated. To this end, sales data is preprocessed, and algorithms are used to find the association rules by using the specific threshold values of Support and Confidence parameters. In this study, we investigated the effects of using fuzzy clustering with association rule mining. A case study from the E-commerce area is selected and sales data for a specific period is analyzed. First ARM is applied to the whole data, then the data is segmented by using Fuzzy Clustering, and ARM is applied to all segments. Later the resulting rules are compared and the effects of segmentation on ARM results are analyzed.

AB - Association rule mining (ARM) refers to a procedure that focuses on finding frequent patterns in various data sources. The most commonly used area is the retail sales data and rules which show an association between sales of two products are investigated. To this end, sales data is preprocessed, and algorithms are used to find the association rules by using the specific threshold values of Support and Confidence parameters. In this study, we investigated the effects of using fuzzy clustering with association rule mining. A case study from the E-commerce area is selected and sales data for a specific period is analyzed. First ARM is applied to the whole data, then the data is segmented by using Fuzzy Clustering, and ARM is applied to all segments. Later the resulting rules are compared and the effects of segmentation on ARM results are analyzed.

KW - Association rule mining

KW - E-Commerce

KW - Fuzzy clustering

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DO - 10.1007/978-3-031-09173-5_15

M3 - Conference contribution

AN - SCOPUS:85135073369

SN - 9783031091728

T3 - Lecture Notes in Networks and Systems

SP - 112

EP - 118

BT - Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Tolga, A. Cagri

A2 - Cebi, Selcuk

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 19 July 2022 through 21 July 2022

ER -

Öztayşi B, Yurdadön P, Onar SÇ. Fuzzy Clustering Based Association Rule Mining: A Case Study on Ecommerce. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Tolga AC, Cebi S, editors, Intelligent and Fuzzy Systems - Digital Acceleration and The New Normal - Proceedings of the INFUS 2022 Conference. Springer Science and Business Media Deutschland GmbH. 2022. p. 112-118. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-09173-5_15

Fuzzy Clustering Based Association Rule Mining: A Case Study on Ecommerce

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