Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy

• Main Authors: Kariyam, Kariyam, Abdurakhman, Abdurakhman, Subanar, Subanar, Utami, Herni, Effendie, Adhitya Ronnie
• 格式: Article PeerReviewed
• 語言: English
• 出版: International Information and Engineering Technology Association (IIETA) 2022
• 主題: Applied Mathematics; Mathematics and Applied Sciences
• 在線閱讀: https://repository.ugm.ac.id/282735/1/Kariyam_PA.pdf; https://repository.ugm.ac.id/282735/; http://iieta.org/journals/mmep; https://doi.org/10.18280/mmep.090622

Most of the existing k-medoid algorithms select the initial medoid randomly or use a specific formula based on the proximity matrix. This study proposes a block-based k- medoids partitioning method for clustering objects. To get the initial medoids, we search for an object representative from the b...