Deep Learning A-Z™: Self Organizing Maps (SOM) - K-Means Clustering (part 2)

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If we choose K = 3 clusters...

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...this correct random initialisation would lead us to...

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…the following three clusters

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…the following three clusters

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But what would happen if we had a bad random initialisation ?

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STEP 1: Choose the number K of clusters
STEP 2: Select at random K points, the centroids (not necessarily from your dataset)
STEP 3: Assign each data point to the closest centroid That forms K clusters
STEP 4: Compute and place the new centroid of each cluster
STEP 5: Reassign each data point to the new closest centroid.
If any reassignment took place, go to STEP 4, otherwise go to FIN.
Your Model is Ready

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STEP 1: Choose the number K of clusters: K = 3

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STEP 2: Select at random K points, the centroids (not necessarily from your dataset)

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STEP 2: Select at random K points, the centroids (not necessarily from your dataset)

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STEP 3: Assign each data point to the closest centroid That forms K clusters

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STEP 3: Assign each data point to the closest centroid That forms K clusters

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STEP 4: Compute and place the new centroid of each cluster

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STEP 5: Reassign each data point to the new closest centroid.
If any reassignment took place, go to STEP 4, otherwise go to FIN.

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STEP 5: Reassign each data point to the new closest centroid.
If any reassignment took place, go to STEP 4, otherwise go to FIN.
Your Model is Ready

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Your Model is Ready
STEP 5: Reassign each data point to the new closest centroid.
If any reassignment took place, go to STEP 4, otherwise go to FIN.

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Your Model is Ready
STEP 5: Reassign each data point to the new closest centroid.
If any reassignment took place, go to STEP 4, otherwise go to FIN.

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Solution K-Means++