Clustering Analysis of Sports Performance Based on Ant Colony Algorithm
Cluster analysis is one of the modes of data mining, which classifies the sample data to different types according to similarity rules. It has also been used in education management field. This paper investigates the principle of k-means clustering algorithm. Because it is easy to converge into local minimum and is also sensitive to noise, isolated point data have a great impact on the average value, an improved clustering algorithm based on ant colony optimization is proposed. The improved algorithm is used in student sports performance management system. It can be concluded that clustering results obtained by the improved algorithm based on ant colony optimization is more scientific, fair and reasonable.