1 / 6

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.

desiree-simpson
Download Presentation

K-means algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related