McCormack's Method

McCormack's Method is a systematic approach for clustering elements into groups based on their similarities. It involves iteratively merging data points or clusters that are closest to each other, measuring similarity through a specific criterion, to form a hierarchy of clusters. This process continues until a desired number of groups or a suitable structure is achieved. The method helps uncover meaningful patterns or categories within complex data, making it useful in fields like data analysis, pattern recognition, and machine learning. Essentially, it organizes data into natural groupings by progressively combining the most similar items.