Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

Algorithm 1

Description of K-means clustering algorithm.

(a)	Randomly initialize the centers of K clusters, that is, select K samples x1, x2,..., xK in the set X as the initial cluster centers.
(b)	Traverse the remaining M-K samples, calculate the distance from the sample to the center of each cluster, and divide the sample into the cluster of the center with the closest distance.
(c)	Calculate and update the center of the cluster according to formula (1).
(d)	If , return to step b). Otherwise, output the clustering result.