PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST
Cluster analysis is a technique for grouping objects in a database based on their similar characteristics. The grouping results are said to be good if each cluster is homogeneous, and can be validated using the silhouette coefficient test. However, the presence of outliers in the data can affect the...
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Universitas Pattimura
2023-09-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8671 |
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| author | Taufiq Akbar Georgina Maria Tinungki Siswanto Siswanto |
| author_facet | Taufiq Akbar Georgina Maria Tinungki Siswanto Siswanto |
| author_sort | Taufiq Akbar |
| collection | DOAJ |
| description | Cluster analysis is a technique for grouping objects in a database based on their similar characteristics. The grouping results are said to be good if each cluster is homogeneous, and can be validated using the silhouette coefficient test. However, the presence of outliers in the data can affect the grouping results, so methods that are robust to outliers are used, such as K-Medoids and Density-Based Spatial Clustering of Applications with Noise. The purpose of this study is to compare the results and performance of the two methods using the silhouette coefficient test on data on human development indicators in South Sulawesi Province in 2021. The results of the analysis show that K-Medoids produced 2 groups, namely the districts/cities group which has indicators of human development that consist of 21 districts/cities, and the high group, which consists of 3 districts/cities, while Density-Based Spatial Clustering of Application with Noise produces 1 group that has the same characteristics, which consists of 19 districts/cities, and the remaining 5 districts/cities are identified as noise. Based on the silhouette coefficient test, K-Medoids have a greater value than Density-Based Spatial Clustering of Application with Noise, namely 0,635 and 0,544, respectively, so that K-Medoids have better performance. |
| format | Article |
| id | doaj-art-5fae982c9abb4664a60a3fb864e9f5e8 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-5fae982c9abb4664a60a3fb864e9f5e82025-08-20T03:36:37ZengUniversitas PattimuraBarekeng1978-72272615-30172023-09-011731605161610.30598/barekengvol17iss3pp1605-16168671PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TESTTaufiq Akbar0Georgina Maria Tinungki1Siswanto Siswanto2Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, IndonesiaCluster analysis is a technique for grouping objects in a database based on their similar characteristics. The grouping results are said to be good if each cluster is homogeneous, and can be validated using the silhouette coefficient test. However, the presence of outliers in the data can affect the grouping results, so methods that are robust to outliers are used, such as K-Medoids and Density-Based Spatial Clustering of Applications with Noise. The purpose of this study is to compare the results and performance of the two methods using the silhouette coefficient test on data on human development indicators in South Sulawesi Province in 2021. The results of the analysis show that K-Medoids produced 2 groups, namely the districts/cities group which has indicators of human development that consist of 21 districts/cities, and the high group, which consists of 3 districts/cities, while Density-Based Spatial Clustering of Application with Noise produces 1 group that has the same characteristics, which consists of 19 districts/cities, and the remaining 5 districts/cities are identified as noise. Based on the silhouette coefficient test, K-Medoids have a greater value than Density-Based Spatial Clustering of Application with Noise, namely 0,635 and 0,544, respectively, so that K-Medoids have better performance.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8671cluster analysisdbscank-medoidssilhouette coefficient |
| spellingShingle | Taufiq Akbar Georgina Maria Tinungki Siswanto Siswanto PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST Barekeng cluster analysis dbscan k-medoids silhouette coefficient |
| title | PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST |
| title_full | PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST |
| title_fullStr | PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST |
| title_full_unstemmed | PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST |
| title_short | PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST |
| title_sort | performance comparison of k medoids and density based spatial clustering of application with noise using silhouette coefficient test |
| topic | cluster analysis dbscan k-medoids silhouette coefficient |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8671 |
| work_keys_str_mv | AT taufiqakbar performancecomparisonofkmedoidsanddensitybasedspatialclusteringofapplicationwithnoiseusingsilhouettecoefficienttest AT georginamariatinungki performancecomparisonofkmedoidsanddensitybasedspatialclusteringofapplicationwithnoiseusingsilhouettecoefficienttest AT siswantosiswanto performancecomparisonofkmedoidsanddensitybasedspatialclusteringofapplicationwithnoiseusingsilhouettecoefficienttest |