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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 assigned genes 4)Repeat 2-3 until convergence Slides from Wash Univ. BIO5488 lecture, 2004

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

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

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

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

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

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CZ5211 Topics in Computational Biology Lecture 4: Clustering Analysis for Microarray Data II Prof. Chen Yu Zong Tel: 6874-6877

CZ5211 Topics in Computational Biology Lecture 4: Clustering Analysis for Microarray Data II Prof. Chen Yu Zong Tel: 6874-6877

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