Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM
In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to pro...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9602526 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849403634701303808 |
|---|---|
| author | Wenshuai Wu Zeshui Xu Gang Kou Yong Shi |
| author_facet | Wenshuai Wu Zeshui Xu Gang Kou Yong Shi |
| author_sort | Wenshuai Wu |
| collection | DOAJ |
| description | In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making support for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment. |
| format | Article |
| id | doaj-art-852a301756f84b8f848264e6d413e80b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-852a301756f84b8f848264e6d413e80b2025-08-20T03:37:12ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/96025269602526Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDMWenshuai Wu0Zeshui Xu1Gang Kou2Yong Shi3Business School, Sichuan University, Chengdu, Sichuan 610065, ChinaBusiness School, Sichuan University, Chengdu, Sichuan 610065, ChinaSchool of Business Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, ChinaCAS Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, ChinaIn many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making support for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment.http://dx.doi.org/10.1155/2020/9602526 |
| spellingShingle | Wenshuai Wu Zeshui Xu Gang Kou Yong Shi Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM Complexity |
| title | Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM |
| title_full | Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM |
| title_fullStr | Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM |
| title_full_unstemmed | Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM |
| title_short | Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM |
| title_sort | decision making support for the evaluation of clustering algorithms based on mcdm |
| url | http://dx.doi.org/10.1155/2020/9602526 |
| work_keys_str_mv | AT wenshuaiwu decisionmakingsupportfortheevaluationofclusteringalgorithmsbasedonmcdm AT zeshuixu decisionmakingsupportfortheevaluationofclusteringalgorithmsbasedonmcdm AT gangkou decisionmakingsupportfortheevaluationofclusteringalgorithmsbasedonmcdm AT yongshi decisionmakingsupportfortheevaluationofclusteringalgorithmsbasedonmcdm |