Machine Learning


K-Means

Abel Sanchez and John R. Williams

K-Means

K-Means Algorithm

  1. Place K points into the space represented by the objects. These points represent initial group of centroids.

  2. Assign each object to the group that has the closest centroid.

  3. When all objects have been assigned, recalculate the positions of the K centroids.

  4. Repeat Steps 2 and 3 until the centroids no longer move.

K-Means Algorithm

Visualization

Active Learning

Write an implementation of K-Means …

Active Learning - Pick Random Means

Active Learning - Assign Centroids

Distance Between Two Points

Active Learning - Get Clusters

Active Learning - Recalculate Centroids

Centroid

THE END