A quality metric for K-Means clustering based on centroid locations
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...
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Main Author: | THULASIDAS, Manoj |
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Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
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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 |
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Institution: | Singapore Management University |
Language: | English |
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