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other:inspect3d:documentation:knowledge_discovery:k-means_clustering [2024/12/20 16:07] – wikisysopother:inspect3d:documentation:knowledge_discovery:k-means_clustering [2026/04/13 20:00] (current) – ↷ Links adapted because of a move operation 47.82.13.138
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 Inspect3D allows users to apply the k-means clustering algorithm to the results of PCA. The dimensionality of the data space is the number of principal components and the user specifies the number of clusters to be found - this is the parameter //k//. Inspect3D allows users to apply the k-means clustering algorithm to the results of PCA. The dimensionality of the data space is the number of principal components and the user specifies the number of clusters to be found - this is the parameter //k//.
  
-  * In the {{:I3D_PCAOptions2.png?30}} [[sift:principal_component_analysis:principal_component_analysis|PCA]] menu there is the option to perform quality assurance (QA) on the results;+  * In the {{:I3D_PCAOptions2.png?30}} [[sift:principal_component_analysis|PCA]] menu there is the option to perform quality assurance (QA) on the results;
   * Selecting {{:I3D_RunPCA.png?30}}**Run QA with PCA** brings up a window with multiple options for QA;   * Selecting {{:I3D_RunPCA.png?30}}**Run QA with PCA** brings up a window with multiple options for QA;
   * The **K-Means** tab allows the user to specify parameter values for the algorithm and then run it on PCA results.   * The **K-Means** tab allows the user to specify parameter values for the algorithm and then run it on PCA results.
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 **Abstract** **Abstract**
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 The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a simple, randomized seeding technique, we obtain an algorithm that is O(log k)-competitive with the optimal clustering. Experiments show our augmentation improves both the speed and the accuracy of k-means, often quite dramatically. The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a simple, randomized seeding technique, we obtain an algorithm that is O(log k)-competitive with the optimal clustering. Experiments show our augmentation improves both the speed and the accuracy of k-means, often quite dramatically.
  
  
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