Vertaile menetelmiä
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| Kyynärpäämenetelmä× | Inertia× | |
|---|---|---|
| Tieteenala | Mallien arviointi | Mallien arviointi |
| Menetelmäperhe | MCDM | MCDM |
| Syntyvuosi≠ | 1953 | 1967 |
| Kehittäjä≠ | Robert Thorndike | Stuart Lloyd, James MacQueen |
| Tyyppi≠ | Heuristic optimization criterion | Clustering quality metric |
| Alkuperäislähde≠ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗ |
| Rinnakkaisnimet≠ | elbow analysis, knee detection | WCSS, within-cluster sum of squares, cluster cohesion |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | 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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