K-means Clustering Algorithm

K-means is one of the most famous clustering algorithm. The steps for this are :

Algorithm

Step 1:


Determine the number of clusters we want in the final classified result and set the number as N. Randomly select N patterns in the whole data bases as the N centroids of N clusters

Step 2:


Classify each pattern to the closest cluster centroid. The closest usually represent the pixel value is similarity, but it still can consider other features.

Step 3:


Recompute the cluster centroids and then there have N centroids of N clusters as we do after Step1

Step 4:


Repeat the iteration of Step 2 to 3 until a convergence criterion is met. The typical convergence criteria are: no reassignment of any pattern from one cluster to another, or the minimal decrease in squared error.

Advantages

  1. K-means algorithm is easy to implement
  2. Its time complexity is O(n), where n is the number of patterns. It is faster than the hierarchical clustering.

disadvantages

  1. The result is sensitive to the selection of the initial random centroids.
  2. We cannot show the clustering details as hierarchical clustering does.
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