Review:
K Means Clustering
overall review score: 4.2
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score is between 0 and 5
K-means clustering is a popular algorithm used in data mining and machine learning to group data points into clusters based on similarity.
Key Features
- Iteratively assigns data points to clusters based on distance from cluster centroids
- Clusters are defined by their centroid, which is the mean of all data points assigned to the cluster
- Requires defining the number of clusters (k) beforehand
- Commonly used for customer segmentation, image compression, and anomaly detection
Pros
- Efficient and scalable for large datasets
- Easy to implement and understand
- Useful for exploratory data analysis
Cons
- Sensitive to initial cluster centers
- May not always find the optimal solution due to local optima
- Does not work well with non-linear boundaries