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Quick takeaways
- Factors to consider when applying K-means clustering include data normalization, dataset size, and the selection of the appropriate value for k.
- K-means clustering can be used to generate additional features from existing datasets, but the usefulness of these labels should be critically evaluated.
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Overview of K-means Clustering
K-means clustering is a method used to partition n data points into k clusters. It works best on data that can be represented as Gaussian blobs. The algorithm, known as Lloyd's algorithm, initializes centroids, assigns data points to the nearest centroid, and recalculates new centroids. While K-means clustering is commonly used, it is important to note that the clustering result is not guaranteed to be the best due to the optimization problem it poses.
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