How can i choose the k initial centroids far away from each other in k-means clustering based image segmentation

5 Ansichten (letzte 30 Tage)
The steps performed for k-means clustering are as follows:
  1. Choose k initial centroids
  2. Compute the distance from each pixel to the centroid
  3. Recalculate the centroids after all the pixels have been assigned
  4. Repeat steps 2 and 3 until the same points are assigned to each cluster in consecutive rounds.
How can i choose the k-initial centroids, such that they are far from each other.

Akzeptierte Antwort

Alok Nimrani
Alok Nimrani am 21 Feb. 2019
You can make use of k-means++ algorithm to choose the initial centroids far away from each other. This algorithm is the one used by default while performing k-means clustering using the k-means function in MATLAB.
Hope this helps.

Weitere Antworten (0)

Kategorien

Mehr zu Statistics and Machine Learning Toolbox finden Sie in Help Center und File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by