Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results
Clustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzin...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10902121/ |
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| author | Ammar Elnour Wencheng Yang Yan Li |
| author_facet | Ammar Elnour Wencheng Yang Yan Li |
| author_sort | Ammar Elnour |
| collection | DOAJ |
| description | Clustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzing result uncertainty. This issue applies to most outcomes obtained from other knowledge discovery techniques, such as machine learning and statistical learning. Traditional statistical methods assume data follows standard distributions, whereas resampling and bootstrapping methods offer more accurate and reliable alternatives. This article introduces a method that employs bootstrap likelihood estimation to infer the uncertainty of generated clustering structures. We first calculated the clustering error in the original dataset and then utilized the proposed method to estimate its nonparametric bootstrapped likelihood. By comparing these two values, we can establish a nonparametric significance testing framework that directly determines the validity of the result. To evaluate the effectiveness of our method, we conducted experiments using synthetic and real datasets. The results demonstrate that our method can successfully validate clustering results. |
| format | Article |
| id | doaj-art-a3a63c2465b542b09cad7fcfaab7c71f |
| institution | Kabale University |
| issn | 2644-1268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-a3a63c2465b542b09cad7fcfaab7c71f2025-08-20T03:40:40ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-01643844810.1109/OJCS.2025.354526110902121Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering ResultsAmmar Elnour0https://orcid.org/0009-0004-0944-2558Wencheng Yang1https://orcid.org/0000-0001-7800-2215Yan Li2https://orcid.org/0000-0002-4694-4926School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, AustraliaSchool of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, AustraliaSchool of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, AustraliaClustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzing result uncertainty. This issue applies to most outcomes obtained from other knowledge discovery techniques, such as machine learning and statistical learning. Traditional statistical methods assume data follows standard distributions, whereas resampling and bootstrapping methods offer more accurate and reliable alternatives. This article introduces a method that employs bootstrap likelihood estimation to infer the uncertainty of generated clustering structures. We first calculated the clustering error in the original dataset and then utilized the proposed method to estimate its nonparametric bootstrapped likelihood. By comparing these two values, we can establish a nonparametric significance testing framework that directly determines the validity of the result. To evaluate the effectiveness of our method, we conducted experiments using synthetic and real datasets. The results demonstrate that our method can successfully validate clustering results.https://ieeexplore.ieee.org/document/10902121/Clusteringvalidity testingrandomnessbootstrap likelihoodsignificance testingstatistical machine learning |
| spellingShingle | Ammar Elnour Wencheng Yang Yan Li Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results IEEE Open Journal of the Computer Society Clustering validity testing randomness bootstrap likelihood significance testing statistical machine learning |
| title | Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results |
| title_full | Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results |
| title_fullStr | Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results |
| title_full_unstemmed | Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results |
| title_short | Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results |
| title_sort | nonparametric bootstrap likelihood estimation to investigate the chance set up on clustering results |
| topic | Clustering validity testing randomness bootstrap likelihood significance testing statistical machine learning |
| url | https://ieeexplore.ieee.org/document/10902121/ |
| work_keys_str_mv | AT ammarelnour nonparametricbootstraplikelihoodestimationtoinvestigatethechancesetuponclusteringresults AT wenchengyang nonparametricbootstraplikelihoodestimationtoinvestigatethechancesetuponclusteringresults AT yanli nonparametricbootstraplikelihoodestimationtoinvestigatethechancesetuponclusteringresults |