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K-means algorithm

K-means algorithm. Pick a number (k) of cluster centers Assign every gene to its nearest cluster center Move each cluster center to the mean of its assigned genes Repeat 2-3 until convergence. Slides from Wash Univ. BIO5488 lecture, 2004. k 1. k 2. k 3. Clustering: Example 2, Step 1.

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K-means algorithm

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  1. K-means algorithm • Pick a number (k) of cluster centers • Assign every gene to its nearest cluster center • Move each cluster center to the mean of its assigned genes • Repeat 2-3 until convergence Slides from Wash Univ. BIO5488 lecture, 2004

  2. k1 k2 k3 Clustering: Example 2, Step 1 Algorithm: k-means, Distance Metric: Euclidean Distance

  3. k1 k2 k3 Clustering: Example 2, Step 2 Algorithm: k-means, Distance Metric: Euclidean Distance

  4. k1 k2 k3 Clustering: Example 2, Step 3 Algorithm: k-means, Distance Metric: Euclidean Distance

  5. k1 k2 k3 Clustering: Example 2, Step 4 Algorithm: k-means, Distance Metric: Euclidean Distance

  6. k1 k2 k3 Clustering: Example 2, Step 5 Algorithm: k-means, Distance Metric: Euclidean Distance

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