DATA SCIENCE WITH MATLAB. UNSUPERVISED LEARNING TECHNIQUES: CLUSTER ANALYSIS

In data science, unsupervised learning uncovers hidden patterns or intrinsic structures within data. This book delves into cluster analysis, a fundamental unsupervised learning technique in data science. It is a key task in exploratory data mining and a common statistical data analysis technique used across many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. The book covers hierarchical and non-hierarchical clustering techniques, as well as clustering using Gaussian mixture models and hidden Markov models. Cluster evaluation and visualization techniques are also addressed. Additionally, the use of neural networks for cluster analysis—including competitive neural networks and Self-Organizing Maps (SOMs)—is explored in depth.