BibTex Citation Data :
@article{J.Gauss26640, author = {Milla Nahdliyah and Tatik Widiharih and Alan Prahutama}, title = {METODE k-MEDOIDS CLUSTERING DENGAN VALIDASI SILHOUETTE INDEX DAN C-INDEX (Studi Kasus Jumlah Kriminalitas Kabupaten/Kota di Jawa Tengah Tahun 2018)}, journal = {Jurnal Gaussian}, volume = {8}, number = {2}, year = {2019}, keywords = {Clustering, k-Medoids, Euclidean distance, Cityblock distance, Silhouette index, C-index, Crime}, abstract = { The k-medoids method is a non-hierarchical clustering to classify n object into k clusters that have the same characteristics. This clustering algorithm uses the medoid as its cluster center. Medoid is the most centrally located object in a cluster, so it’s robust to outliers. In cluster analysis the objects are grouped by the similarity. To measure the similarity, it can be used distance measures, euclidean distance and cityblock distance. The distance that is used in cluster analysis can affect the clustering results. Then, to determine the quality of the clustering results can be used the internal criteria with silhouette width and C-index. In this research the k-medoids method to classify of regencies/cities in Central Java based on type and number of crimes. The optimal cluster at k= 4 use euclidean distance, where the silhouette index= 0,3862593 and C-index= 0,043893. Keywords: Clustering, k-Medoids, Euclidean distance, Cityblock distance, Silhouette index, C-index, Crime }, issn = {2339-2541}, pages = {161--170} doi = {10.14710/j.gauss.8.2.161-170}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/26640} }
Refworks Citation Data :
The k-medoids method is a non-hierarchical clustering to classify n object into k clusters that have the same characteristics. This clustering algorithm uses the medoid as its cluster center. Medoid is the most centrally located object in a cluster, so it’s robust to outliers. In cluster analysis the objects are grouped by the similarity. To measure the similarity, it can be used distance measures, euclidean distance and cityblock distance. The distance that is used in cluster analysis can affect the clustering results. Then, to determine the quality of the clustering results can be used the internal criteria with silhouette width and C-index. In this research the k-medoids method to classify of regencies/cities in Central Java based on type and number of crimes. The optimal cluster at k= 4 use euclidean distance, where the silhouette index= 0,3862593 and C-index= 0,043893.
Keywords: Clustering, k-Medoids, Euclidean distance, Cityblock distance, Silhouette index, C-index, Crime
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