Fundamentals of clustering algorithms

Clustering algorithms group similar data points together based on certain features, revealing natural patterns or categories within data. They analyze the data to find points that are more alike and group them into clusters, with each cluster representing a segment sharing common characteristics. This process helps in understanding complex data without predefined labels, making it useful for tasks like customer segmentation, image analysis, or anomaly detection. Key principles include measuring similarity, choosing how many clusters to form, and iteratively refining groups to best reflect real-world relationships.