Analysis of Key Factors Affecting Undergraduate Entrepreneurship Ability from a Big Data Perspective

Algorithm 1

Input: the number of clusters in the sample dataset is .

Output: centers of clustering and feature weights

   1) According to the steps given in the initial cluster center optimization, select cluster centers

   2) Calculate the feature difference degree of the dataset according to Equation (1)

   3) Calculate the maximum feature ratio . When , it indicates that there is a problem of maximum and minimum feature weights; then Equation (3) is selected to calculate feature weights ; when , then Equation (2) is selected to calculate feature weights

   4) Using Equation (1), calculate the similarity between the sample and the central point (4), take the smallest similarity as the sample attribution category, and assign the sample to the category

   5) According to the samples divided by , calculate the average value of each feature of similar samples and update the cluster center

   6) Calculate the variables in Equation (4) and repeat Step 2-Step 5 until the SSE remains unchanged or reaches the specified number of iterations.