Understanding clustering – basic concepts and methods

Clustering is a fundamental concept in data analysis, aiming to identify meaningful groupings or patterns within a dataset. It involves the partitioning of data points into distinct clusters based on their similarity or proximity to each other. In both clustering and classification, our goal is to discover the underlying rules that enable us to assign observations to the correct class. However, clustering differs from classification as it requires identifying a meaningful subdivision of classes as well. In classification, we benefit from the target variable, which provides the classification information in the training set. In contrast, clustering lacks such additional information, necessitating the deduction of classes by analyzing the spatial distribution of the data. Dense areas in the data correspond to groups of similar observations. If we can identify observations that are like each other but distinct from those in...