The K-Means Clustering method: for numerical attributes Given k, the k-means algorithm is implemented in four steps Partition objects into k non-empty subsets Compute seed points as the centroids of the clusters of the current partition the centroid is the center, i. e. mean point, of the cluster) Assign each object to the cluster with the nearest seed point go back to Step 2, stop when no more new assignment9 The K-Means Clustering Method: for numerical attributes ◼ Given k, the k-means algorithm is implemented in four steps: ◼ Partition objects into k non-empty subsets ◼ Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean point, of the cluster) ◼ Assign each object to the cluster with the nearest seed point ◼ Go back to Step 2, stop when no more new assignment