방법 비교

	관성 (Inertia)	Davies-Bouldin Index	던 지수
분야	모델 평가	모델 평가	모델 평가
계열	MCDM	MCDM	MCDM
기원 연도	1967	1979	1974
창시자	Stuart Lloyd, James MacQueen	David L. Davies, Donald W. Bouldin	Joseph C. Dunn
유형	Clustering quality metric	Cluster quality metric	Cluster quality metric
원전	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.	Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.	Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.
별칭	WCSS, within-cluster sum of squares, cluster cohesion	DBI, Davies Bouldin index	Dunn's index, separation coefficient
관련	5	5	5
요약	Inertia, also called Within-Cluster Sum of Squares (WCSS), is a measure of cluster cohesion that quantifies how tightly points are grouped around their cluster centroids. Lower values indicate more compact, cohesive clusters. Inertia is the primary objective function for k-means clustering and has been a fundamental metric since the method's introduction.	The Davies-Bouldin Index, introduced by Davies and Bouldin in 1979, is a metric for evaluating clustering quality based on the average similarity between each cluster and its most similar neighboring cluster. Lower values indicate better clustering, with a minimum of 0 representing perfectly separated, non-overlapping clusters.	The Dunn Index, introduced by Joseph C. Dunn in 1974, is a metric that captures cluster quality by measuring the ratio of the minimum between-cluster distance to the maximum within-cluster diameter. Higher values indicate well-separated and compact clusters, with better clustering quality.