K-means Clustering Based on Improved Sparrow Search
Aiming at the problems that traditional K-means clustering algorithm relies on the initial solution and is easy to fall into local optima,a K-means clustering based on the improved sparrow search algorithm is proposed.First,introduce the fractional calculus into the sparrow algorithm,and update the position of the sparrow with an adaptive fractional order to improve the convergence accuracy of the algorithm;Secondly,perform elite reverse learning on the sparrow population to enhance the diversity of the population and expand the scope of the search area;Then,adaptive t distribution mutation is performed on the position of the sparrow to avoid the algorithm from falling into the local optimum;Finally,the performance of the proposed improved algorithm is verified in 8 benchmark functions and applied to K-means clustering.The clustering simulation experiment on the UCI data set shows that the K-means clustering based on the improved sparrow search algorithm can effectively improve the clustering quality and algorithm stability.
sparrow algorithmadaptive fractional orderelite reverse learningadaptive t distributionK-means clustering
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