The performance of k-means clustering method based on robust principal components
The k-means clustering method is the most widely used method to group n observations into k clusters. It is now evident that clustering results can be improved by applying classical principal component analysis (PCA) with the k-means clustering algorithm. However, the clustering results of PCA with...
| Main Authors: | , , |
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| Format: | Article |
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Pushpa Publishing House
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/74236/ |
| _version_ | 1848857460301889536 |
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| author | Kadom, Ahmed Midi, Habshah Rana, Sohel |
| author_facet | Kadom, Ahmed Midi, Habshah Rana, Sohel |
| author_sort | Kadom, Ahmed |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The k-means clustering method is the most widely used method to group n observations into k clusters. It is now evident that clustering results can be improved by applying classical principal component analysis (PCA) with the k-means clustering algorithm. However, the clustering results of PCA with k-means are adversely affected by the presence of outliers in a data set. To remedy this problem, we proposed to integrate robust principal component analysis (RPCA) with the k-means algorithm. Simulation study and real examples are carried out to compare the performance of the classical k-means, k-means based on PCA and k-means based on RPCA. The findings indicate that the k-means based on RPCA outperforms the other two methods. |
| first_indexed | 2025-11-15T11:57:54Z |
| format | Article |
| id | upm-74236 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T11:57:54Z |
| publishDate | 2018 |
| publisher | Pushpa Publishing House |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-742362024-05-16T07:11:30Z http://psasir.upm.edu.my/id/eprint/74236/ The performance of k-means clustering method based on robust principal components Kadom, Ahmed Midi, Habshah Rana, Sohel The k-means clustering method is the most widely used method to group n observations into k clusters. It is now evident that clustering results can be improved by applying classical principal component analysis (PCA) with the k-means clustering algorithm. However, the clustering results of PCA with k-means are adversely affected by the presence of outliers in a data set. To remedy this problem, we proposed to integrate robust principal component analysis (RPCA) with the k-means algorithm. Simulation study and real examples are carried out to compare the performance of the classical k-means, k-means based on PCA and k-means based on RPCA. The findings indicate that the k-means based on RPCA outperforms the other two methods. Pushpa Publishing House 2018 Article PeerReviewed Kadom, Ahmed and Midi, Habshah and Rana, Sohel (2018) The performance of k-means clustering method based on robust principal components. Far East Journal of Mathematical Sciences (FJMS), 103 (11). 1757 - 1767. ISSN 0972-0871 http://www.pphmj.com/abstract/11654.htm 10.17654/ms103111757 |
| spellingShingle | Kadom, Ahmed Midi, Habshah Rana, Sohel The performance of k-means clustering method based on robust principal components |
| title | The performance of k-means clustering method based on robust principal components |
| title_full | The performance of k-means clustering method based on robust principal components |
| title_fullStr | The performance of k-means clustering method based on robust principal components |
| title_full_unstemmed | The performance of k-means clustering method based on robust principal components |
| title_short | The performance of k-means clustering method based on robust principal components |
| title_sort | performance of k-means clustering method based on robust principal components |
| url | http://psasir.upm.edu.my/id/eprint/74236/ http://psasir.upm.edu.my/id/eprint/74236/ http://psasir.upm.edu.my/id/eprint/74236/ |