Multi-factor evaluation of clustering methods for e-commerce application
This research aimed to investigate the application of Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making to select the optimal clustering for e-commerce customer segmentation. In...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524001257 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850119359306924032 |
|---|---|
| author | Adam Wasilewski Krzysztof Juszczyszyn Vera Suryani |
| author_facet | Adam Wasilewski Krzysztof Juszczyszyn Vera Suryani |
| author_sort | Adam Wasilewski |
| collection | DOAJ |
| description | This research aimed to investigate the application of Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making to select the optimal clustering for e-commerce customer segmentation. In this context, clustering as an unsupervised machine learning method offered a way to overcome the limitations of traditional grouping, particularly by providing the ability to capture the diverse needs of consumers. A total of five different clustering methods were considered based on the behavioral data of e-commerce customers. Even though the analyzed algorithms were well-known and widely used, the comprehensive and multidirectional comparison was not trivial. Selected approaches were evaluated on the basis of twelve indicators (decision criteria), divided into four characteristics that take into account both the out-of-context aspects of clustering and the requirements arising from the context of using the clustering results. The results showed consistent outcomes from both analyzed Multi-Criteria Decision Methods, with some notable differences. The methods obtained the same ranking of the top three clustering algorithms (K-median - BIRCH - K-means). However, the TOPSIS and VIKOR sensitivity analysis recommended K-means in 87% of the cases and 60% of the variants verified, respectively. The parameterization of the decision factors had a significant impact on the final ranking of clustering options. This research demonstrated the practical application of the decision methods in selecting the best clustering for multivariate user interfaces to improve personalization in e-commerce. |
| format | Article |
| id | doaj-art-bcf84f01d2454ae88c03a21739009ec1 |
| institution | OA Journals |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-bcf84f01d2454ae88c03a21739009ec12025-08-20T02:35:39ZengElsevierEgyptian Informatics Journal1110-86652024-12-012810056210.1016/j.eij.2024.100562Multi-factor evaluation of clustering methods for e-commerce applicationAdam Wasilewski0Krzysztof Juszczyszyn1Vera Suryani2Faculty of Management, Wroclaw University of Science and Technology, Wyb. Wyspiańskiego 27, Wrocław, 50-370, Poland; Corresponding author.Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wyb. Wyspiańskiego 27, Wrocław, 50-370, PolandSchool of Computing, Telkom University, Jl. Telekomunikasi, No. 1, Bandung, 40-257, IndonesiaThis research aimed to investigate the application of Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making to select the optimal clustering for e-commerce customer segmentation. In this context, clustering as an unsupervised machine learning method offered a way to overcome the limitations of traditional grouping, particularly by providing the ability to capture the diverse needs of consumers. A total of five different clustering methods were considered based on the behavioral data of e-commerce customers. Even though the analyzed algorithms were well-known and widely used, the comprehensive and multidirectional comparison was not trivial. Selected approaches were evaluated on the basis of twelve indicators (decision criteria), divided into four characteristics that take into account both the out-of-context aspects of clustering and the requirements arising from the context of using the clustering results. The results showed consistent outcomes from both analyzed Multi-Criteria Decision Methods, with some notable differences. The methods obtained the same ranking of the top three clustering algorithms (K-median - BIRCH - K-means). However, the TOPSIS and VIKOR sensitivity analysis recommended K-means in 87% of the cases and 60% of the variants verified, respectively. The parameterization of the decision factors had a significant impact on the final ranking of clustering options. This research demonstrated the practical application of the decision methods in selecting the best clustering for multivariate user interfaces to improve personalization in e-commerce.http://www.sciencedirect.com/science/article/pii/S1110866524001257TOPSISVIKORE-commerceMachine learningClustering |
| spellingShingle | Adam Wasilewski Krzysztof Juszczyszyn Vera Suryani Multi-factor evaluation of clustering methods for e-commerce application Egyptian Informatics Journal TOPSIS VIKOR E-commerce Machine learning Clustering |
| title | Multi-factor evaluation of clustering methods for e-commerce application |
| title_full | Multi-factor evaluation of clustering methods for e-commerce application |
| title_fullStr | Multi-factor evaluation of clustering methods for e-commerce application |
| title_full_unstemmed | Multi-factor evaluation of clustering methods for e-commerce application |
| title_short | Multi-factor evaluation of clustering methods for e-commerce application |
| title_sort | multi factor evaluation of clustering methods for e commerce application |
| topic | TOPSIS VIKOR E-commerce Machine learning Clustering |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524001257 |
| work_keys_str_mv | AT adamwasilewski multifactorevaluationofclusteringmethodsforecommerceapplication AT krzysztofjuszczyszyn multifactorevaluationofclusteringmethodsforecommerceapplication AT verasuryani multifactorevaluationofclusteringmethodsforecommerceapplication |