The book "Similarity-Based Clustering" offers a comprehensive introduction to methods of similarity-based learning, which prove to be a valuable tool for the analysis, visualization, and inspection of large datasets. It addresses the combination of intuitive, easily understandable principles such as distance-based classification and Hebbian learning with a variety of customizable design options tailored to specific problems. This is particularly significant in the fields of medicine, biology, and medical bioinformatics, where high-dimensional and often noisy data from clinical measurements like EEG or fMRI are obtained. The book highlights the challenges and opportunities arising from the rapid development of new technologies that enable efficient and automated collection of high-quality data. It demonstrates how machine learning can help extract and interpret relevant information to promote a better understanding of biological systems and facilitate reliable diagnoses.