Description: Affinity clustering is an unsupervised learning technique used to group data points based on their similarity or affinity to one another. This technique focuses on identifying patterns and relationships in datasets without the need for predefined labels. Through specific algorithms, affinity clustering evaluates the proximity of data in a multidimensional space, allowing points that share similar characteristics to cluster together. The main features of this technique include its ability to handle high-dimensional data and its flexibility to adapt to different data shapes. Additionally, affinity clustering can use various distance metrics, such as Euclidean distance or Manhattan distance, to determine the similarity between points. This technique is particularly relevant in data analysis, as it enables analysts to uncover hidden structures in data, facilitating informed decision-making. In summary, affinity clustering is a powerful tool in the field of machine learning that helps to break down and understand large volumes of information effectively.
