The document describes the K-Means clustering algorithm. It involves choosing the number of clusters K, selecting initial centroid points, assigning data points to the closest centroid, recomputing the centroids, and reassigning points in an iterative process until centroids are stable. The algorithm partitions a dataset into K number of groups based on feature similarity between data points so that the resulting internal homogeneity is high.

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Before K-Means

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Before K-Means After K-Means
K-Means

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Before K-Means After K-Means
K-Means

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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 = 2

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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 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.

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

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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.

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

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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.

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

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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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FIN: Your Model Is Ready

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FIN: Your Model Is Ready

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

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FIN: Your Model Is Ready