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| Индекс на Дейвис-Болдин× | Gap Statistic× | Инерция× | |
|---|---|---|---|
| Област | Оценка на модели | Оценка на модели | Оценка на модели |
| Семейство | MCDM | MCDM | MCDM |
| Година на възникване≠ | 1979 | 2001 | 1967 |
| Създател≠ | David L. Davies, Donald W. Bouldin | Robert Tibshirani, Guenther Walther, Trevor Hastie | Stuart Lloyd, James MacQueen |
| Тип≠ | Cluster quality metric | Statistical criterion | Clustering quality metric |
| Основополагащ източник≠ | Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227. DOI ↗ | Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗ |
| Други названия≠ | DBI, Davies Bouldin index | gap index, Tibshirani gap statistic | WCSS, within-cluster sum of squares, cluster cohesion |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | 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 Gap Statistic, developed by Tibshirani, Walther, and Hastie in 2001, is a principled statistical method for determining the optimal number of clusters in a dataset. It compares the observed within-cluster sum of squares to the expected value under a null hypothesis of no clustering structure, providing a theoretically grounded approach to cluster number selection. | 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. |
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