[Retracted] An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

Algorithm 1

K-means clustering process.

	Input: number of clusters K, data set D
	Output: K clusters.
	Algorithm steps:
	Step 1: Take K, which means we will divide the data set into K groups.
	Step 2: Randomly select K points from the data set as the initial clustering centers.
	Step 3: Calculate the distances between all points and the K cluster centers and put the samples into the class with the center with the shortest distance.
	Step 4: Calculate the average coordinates of the data points in each class cluster to update the center of the cluster.
	Step 5: Repeat steps (3) and (4) until the cluster center remains unchanged.