Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm
The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO al...
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| Published in: | Applied soft computing Vol. 113; p. 107924 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.12.2021
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| ISSN: | 1568-4946, 1872-9681 |
| Online Access: | Get full text |
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| Abstract | The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum; thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the K-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the K-means cluster centres (KM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare KM-ALPSO on five UCI datasets. Finally, the improved KM-ALPSO (IKM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IKM-ALPSO for customer segmentation.
•A new ALPSO algorithm is proposed to improve the optimization accuracy of PSO.•A new KM-ALPSO is proposed to avert the dependence of K-means on the initial centres.•A new IKM-ALPSO algorithm is proposed to address the issue of clustering mixed data.•All results show that the three proposed methods outperform existing models.•The results demonstrate the practicability of IKM-ALPSO for customer segmentation. |
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| AbstractList | The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum; thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the K-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the K-means cluster centres (KM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare KM-ALPSO on five UCI datasets. Finally, the improved KM-ALPSO (IKM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IKM-ALPSO for customer segmentation.
•A new ALPSO algorithm is proposed to improve the optimization accuracy of PSO.•A new KM-ALPSO is proposed to avert the dependence of K-means on the initial centres.•A new IKM-ALPSO algorithm is proposed to address the issue of clustering mixed data.•All results show that the three proposed methods outperform existing models.•The results demonstrate the practicability of IKM-ALPSO for customer segmentation. |
| ArticleNumber | 107924 |
| Author | Tian, Dong Chu, Xiaoquan Li, Yue Mu, Weisong Feng, Jianying |
| Author_xml | – sequence: 1 givenname: Yue orcidid: 0000-0003-1392-2074 surname: Li fullname: Li, Yue email: 18800146617@163.com organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China – sequence: 2 givenname: Xiaoquan surname: Chu fullname: Chu, Xiaoquan email: chuxiaoquan1994@163.com organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China – sequence: 3 givenname: Dong orcidid: 0000-0002-4759-5334 surname: Tian fullname: Tian, Dong email: td_tiandong@cau.edu.cn organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China – sequence: 4 givenname: Jianying orcidid: 0000-0002-1048-7524 surname: Feng fullname: Feng, Jianying email: fjying@cau.edu.cn organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China – sequence: 5 givenname: Weisong surname: Mu fullname: Mu, Weisong email: wsmu@cau.edu.cn organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China |
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| Keywords | Customer segmentation K-means clustering algorithm Mixed data Particle swarm optimization algorithm Adaptive parameter learning |
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