Abstract
The random selection of initial clustering centers, outliers, and the differences between attributes will affect the clustering effect of k-means. This article first uses the elbow method to determine the number of categories and then uses the square difference radius method to select the cluster seed center to optimize the cluster center’s reselection. Finally, the entropy method is used to calculate the difference between attributes. The results show that when the number of categories remains the same and abnormal data is added, the improved clustering algorithm from multiple perspectives is more accurate and stable for small sample data with small dimensions and large differences between categories.
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