cloudsandai

K-Means Clustering: An Interactive Lab

Explore unsupervised learning by assigning observations to clusters, adjusting k, and watching centroids move with each iteration.

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Interactive model

K-Means Clustering

Choose a cluster count and iteration limit to watch centroids group unlabeled observations by similarity.

Study hours 1, practice tests 1, cluster 1Study hours 1, practice tests 3, cluster 1Study hours 1, practice tests 5, cluster 1Study hours 1, practice tests 7, cluster 2Study hours 1, practice tests 9, cluster 2Study hours 3, practice tests 1, cluster 1Study hours 3, practice tests 3, cluster 1Study hours 3, practice tests 5, cluster 1Study hours 3, practice tests 7, cluster 2Study hours 3, practice tests 9, cluster 2Study hours 5, practice tests 1, cluster 1Study hours 5, practice tests 3, cluster 1Study hours 5, practice tests 5, cluster 2Study hours 5, practice tests 7, cluster 2Study hours 5, practice tests 9, cluster 2Study hours 7, practice tests 1, cluster 3Study hours 7, practice tests 3, cluster 3Study hours 7, practice tests 5, cluster 3Study hours 7, practice tests 7, cluster 2Study hours 7, practice tests 9, cluster 2Study hours 9, practice tests 1, cluster 3Study hours 9, practice tests 3, cluster 3Study hours 9, practice tests 5, cluster 3Study hours 9, practice tests 7, cluster 3Study hours 9, practice tests 9, cluster 2Cluster 1 centroidCluster 2 centroidCluster 3 centroidyouStudy hoursPractice tests
Cluster 1Cluster 2Cluster 3

Click anywhere on the chart to place a point and explore its result.

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