方法对比

	惯性	Calinski-Harabasz指数	戴维斯-布尔丁指数
领域	模型评估	模型评估	模型评估
方法族	MCDM	MCDM	MCDM
起源年份	1967	1974	1979
提出者	Stuart Lloyd, James MacQueen	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin
类型	Clustering quality metric	Cluster quality metric	Cluster quality metric
开创性文献	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.	Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27.	Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.
别名	WCSS, within-cluster sum of squares, cluster cohesion	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index
相关	5	5	5
摘要	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 Calinski-Harabasz Index, also called the Variance Ratio Criterion, was introduced by Calinski and Harabasz in 1974. It is a metric that measures the ratio of between-cluster variance to within-cluster variance, adjusted for the number of clusters and data points. Higher values indicate better-separated, more compact clusters.	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.