Machine Learning AlgorithmsIntermediate
K-Means Clustering
Fundamental unsupervised machine learning algorithm for partitioning data into k clusters. Iteratively assigns points to nearest centroids and updates centroid positions. Widely used in customer segmentation, image compression, and exploratory data analysis.
#machine-learning#clustering#unsupervised#k-means++#data-science
Complexity Analysis
Time (Average)
O(n × k × i × d)Expected case performance
Space
O(n + k)Memory requirements
Time (Best)
O(n × k × i × d)Best case performance
Time (Worst)
O(n × k × i × d)Worst case performance
11 data points
How it works
- • Partition n points into k clusters
- • Minimize within-cluster variance
- • Iterative algorithm
- • O(n × k × iterations) time complexity
- • Used in data mining and pattern recognition
Step: 1 / 0
500ms
SlowFast
Keyboard Shortcuts
Space Play/Pause← → StepR Reset1-4 Speed
Real-time Statistics
Algorithm Performance Metrics
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Algorithm Visualization
Step 1 of 0
Initialize array to begin
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