Building Models with Distance Metrics

This chapter will cover the following recipes:

• Using k-means to cluster data
• Optimizing the number of centroids
• Assessing cluster correctness
• Using MiniBatch k-means to handle more data
• Quantizing an image with k-means clustering
• Finding the closest objects in the feature space
• Probabilistic clustering with Gaussian Mixture Models
• Using k-means for outlier detection
• Using KNN for regression