无监督聚类：K均值算法

1.K均值聚类

K-means clustering represents a fundamental category of unsupervised learning techniques. This method systematically groups similar data points into clusters, thereby identifying cohesive and distinct clusters within a dataset. Specifically, the algorithm quantifies the dispersion of each cluster y through the following formula:

C(y) = \frac{1}{N}\sum_{i=1}^{N} \min_{c \neq y} ||x_i - x_c||^2

This formula calculates the average squared distance between each data point and all other points in its nearest cluster, effectively measuring how tightly