Clustering with Unsupervised Learning

In this chapter, we will cover the following recipes:

• Clustering data using the k-means algorithm
• Compressing an image using vector quantization
• Grouping data using agglomerative clustering
• Evaluating the performance of clustering algorithms
• Estimating the number of clusters using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm
• Finding patterns in stock market data
• Building a customer segmentation model
• Using autoencoders to reconstruct handwritten digit images