A quality metric for K-Means clustering based on centroid locations

• Main Author: THULASIDAS, Manoj
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: K-Means clustering; Quality metrics; K selection problem; Number of clusters; Computer Engineering; Numerical Analysis and Scientific Computing; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/7744; https://ink.library.smu.edu.sg/context/sis_research/article/8747/viewcontent/A_quality_metric_for_k_means_clustering_based_on_centroid_locations.pdf
• Institution: Singapore Management University

K-Means clustering algorithm does not offer a clear methodology to determine the appropriate number of clusters; it does not have a built-in mechanism for K selection. In this paper, we present a new metric for clustering quality and describe its use for K selection. The proposed metric, based on th...