K-means Algorithm: The Most Classic Partition-Based Clustering Method

The K-means algorithm represents the most fundamental partition-based clustering approach and ranks among the top ten classic data mining algorithms. Its core concept involves clustering data points around k centroids in space, iteratively updating cluster centers until optimal results are achieved. Implementation typically requires specifying the number of clusters (k), initial centroid selection, and distance metric calculation.