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K-means algorithm 1)Pick a number (k) of cluster centers 2)Assign every gene to its nearest cluster center 3)Move each cluster center to the mean of its.

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Presentation on theme: "K-means algorithm 1)Pick a number (k) of cluster centers 2)Assign every gene to its nearest cluster center 3)Move each cluster center to the mean of its."— Presentation transcript:

1 K-means algorithm 1)Pick a number (k) of cluster centers 2)Assign every gene to its nearest cluster center 3)Move each cluster center to the mean of its assigned genes 4)Repeat 2-3 until convergence Slides from Wash Univ. BIO5488 lecture, 2004

2 Clustering: Example 2, Step 1 Algorithm: k-means, Distance Metric: Euclidean Distance k1k1 k2k2 k3k3

3 Clustering: Example 2, Step 2 Algorithm: k-means, Distance Metric: Euclidean Distance k1k1 k2k2 k3k3

4 Clustering: Example 2, Step 3 Algorithm: k-means, Distance Metric: Euclidean Distance k1k1 k2k2 k3k3

5 Clustering: Example 2, Step 4 Algorithm: k-means, Distance Metric: Euclidean Distance k1k1 k2k2 k3k3

6 Clustering: Example 2, Step 5 Algorithm: k-means, Distance Metric: Euclidean Distance k1k1 k2k2 k3k3

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