เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| สถิติกระโดด (Gap Statistic)× | ดัชนี Calinski-Harabasz× | วิธีข้อศอก× | |
|---|---|---|---|
| สาขาวิชา | การประเมินแบบจำลอง | การประเมินแบบจำลอง | การประเมินแบบจำลอง |
| ตระกูล | MCDM | MCDM | MCDM |
| ปีกำเนิด≠ | 2001 | 1974 | 1953 |
| ผู้ริเริ่ม≠ | Robert Tibshirani, Guenther Walther, Trevor Hastie | Tadeusz Calinski, Jerzy Harabasz | Robert Thorndike |
| ประเภท≠ | Statistical criterion | Cluster quality metric | Heuristic optimization criterion |
| แหล่งต้นตำรับ≠ | 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 ↗ | Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. link ↗ |
| ชื่อเรียกอื่น≠ | gap index, Tibshirani gap statistic | variance ratio criterion, pseudo F-statistic, CH index | elbow analysis, knee detection |
| ที่เกี่ยวข้อง | 5 | 5 | 5 |
| สรุป≠ | 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. | 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 Elbow Method is a heuristic for selecting the optimal number of clusters in partitional clustering. Introduced by Robert Thorndike in 1953, it involves fitting clustering models for increasing numbers of clusters and plotting the within-cluster sum of squares (WCSS) against the number of clusters. The 'elbow' occurs where the rate of WCSS decrease sharply changes, suggesting an optimal cluster count. |
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