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K-Means Clustering
Randomly Select Each Object Assigned Clusters Centres Updated
K-Clusters (K=2) To Similar Centroid Depending On Renewed Cluster
Mean
100 100 100
90 90 90
80 80 80
70 70 70
60 60 60
50 50 50
40 40 40
30 30 30
20 20 20
10 10 10
0 0 0
0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
Re-Assign Re-Assign
Data Points Data Points
Update Cluster
100 Centres 100
90 90
80 80
70 70
60 60
50 50
40 40
30 30
20 20
10 Iterative 10
0 0
0 10 20 30 40 50 60 70 80 90 100 Process 0 10 20 30 40 50 60 70 80 90 100
Experiential Learning
Video Session
Scan the QR code or visit the following link to watch the video: StatQuest: K-means Clustering
https://www.youtube.com/watch?v=4b5d3muPQmA
After watching the video, answer the following question:
What do you mean by K-means clustering according to the video?
Advantages of K-Means
Some of the advantages of K-Means Clustering are:
• Quite simple to implement.
• Can handle large data sets.
• Can give initial positions to centroids (randomly).
• Easily adapts to new data.
• Can easily adapt to clusters of different shapes and sizes, like elliptical clusters.
312 Touchpad Artificial Intelligence (Ver. 2.0)-XI

