K-Means is an unsupervised learning algorithm that partitions data into K clusters. Watch as the algorithm iteratively assigns points to the nearest centroid and updates centroid positions until convergence.
Algorithm Steps:
1
Initialize K random centroids
2
Assign each point to nearest centroid
3
Update centroids to cluster means
4
Repeat until convergence
Iterations
0
Total Inertia
-
Status
Ready
Understanding K-Means Clustering
K-Means clustering aims to partition n observations into k clusters where each observation belongs to the cluster with the nearest mean (centroid). The algorithm works as follows:
Initialization: Choose K initial cluster centers (centroids), either randomly or using methods like K-means++
Assignment: Assign each data point to the nearest centroid based on Euclidean distance
Update: Recalculate centroids as the mean of all points assigned to each cluster
Repeat: Continue steps 2-3 until centroids no longer move significantly
Key Concepts:
Inertia: Sum of squared distances from each point to its assigned centroid
Convergence: Algorithm stops when centroids stabilize
Local Optima: Results can vary based on initial centroid positions