BRAIN Journal-A New Challenge for Information Mining-Figure 3: Clustered Instances

<p>&nbsp;We tested the algorithm with different values of K, to find the optimal centroids. In general, as you know, there is no method for determining the exact value of K, but an accurate estimate can be obtained, for example, monitoring the value of the sum of squared error (SSE) for some values of k (for example 2, 4, 6, 8, etc.). The SSE is defined as the sum of the squared distance between each member of the cluster and its centroid. Mathematically, we can write (1): ( , ) (1) 1 2     K i c x i i SSE dist x c In our case we estimated in k = 8 the best number of cluster. We obtained the following clustered instances.</p>