Similarity based clustering

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Similarity-based learning methods serve as an intuitive and flexible toolbox for mining, visualizing, and inspecting large data sets. They merge simple, understandable principles like distance-based classification and Hebbian learning with diverse, problem-specific design choices, including data-optimum topology and similarity measures. In fields such as medicine, biology, and medical bioinformatics, increasing amounts of data emerge from clinical measurements, including EEG and fMRI studies for brain activity monitoring, mass spectrometry for protein detection, and microarray profiles for gene expression analysis. This data is often high-dimensional, noisy, and challenging to analyze with traditional methods. Concurrently, advancements in technology are enabling high-resolution spectra and high-throughput screening, leading to the efficient gathering of vast amounts of high-quality data with significant information potential. There is a pressing need for machine learning methods that can automatically extract and interpret relevant information, facilitating the understanding of biological systems and aiding in reliable diagnosis and treatment of diseases like cancer. These scenarios present unique challenges to learning systems, particularly in the medical domain, paving the way for new algorithmic designs and theoretical advancements.