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KMEANS CLUSTERING.

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Presentation on theme: "KMEANS CLUSTERING."— Presentation transcript:

1 KMEANS CLUSTERING

2 KMeans Clustering

3 KMeans Clustering RANDOM INITITALIZE TRAP

4 KMeans Clustering

5 KMeans Clustering STEP BY STEP STEP 1 K = 3 | Number of Clusters

6 KMeans Clustering STEP 2 K = 3 | Assign the Centroids

7 KMeans Clustering STEP 3 K = 3 | Assign each data the Closest centroid

8 KMeans Clustering STEP 4
K = 3 | Compute and Place the new Centroid of each cluster

9 KMeans Clustering STEP 5
K = 3 | Reassign each datapoint to closest centroid. If any reassignment took place, GO TO Step 4, else FIN

10 KMeans Clustering WITHIN CLUSTER SUM of SQUARES

11 KMeans Clustering CLUSTER FORMATIONS

12 KMeans Clustering ELBOW CURVE


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