Research And Application On K-means Clustering Improved Algorithm

In recent years,finding the most concerned information among the huge data information has become a very important research hotspot.With the increasing expansion of computer network technology,information data and the continuous development of database technology.On this basis,the emergence of data mining technology has attracted great attention of the information industry.Clustering analysis technology is a technology that divides data sets into multiple clusters to ensure that data features in the same cluster are as consistent as possible and that data features among different clusters are as different as possible.There are many kinds of clustering techniques in data mining.Among many algorithms,K-means algorithm is widely used because of its simple process and good convergence.In addition,it includes injection hierarchical clustering algorithm,constraint-based clustering algorithm,clustering algorithm applied in high-dimensional data,and so on.In the traditional data mining field,the stability of K-means algorithm in the process of data acquisition and analysis is difficult to be guaranteed,because the clustering results are highly sensitive to the initial center selection,and the K value is difficult to determine.This paper proposes an improved clustering algorithm based on K-means algorithm,which retains the advantages of the traditional K-means algorithm.At the same time,the principle of the improved algorithm is similar to that of K-means algorithm.In this paper,the improved K-means algorithm is used to complete the technical statistical analysis of NBA players.In addition,the K-means algorithm in the application process of the initial clustering center dependence is improved.The improved clustering center selection is more flexible.At the same time,through the optimization of the initial clustering center selection,the clustering efficiency of K-means algorithm and the stability of clustering results are improved.Finally,the improved K-means algorithm is described in detail,and the execution process of the algorithm is clearly demonstrated.Finally,the advantages and disadvantages of the traditional K-means algorithm are explained based on the execution process and results of the algorithm.In order to verify the actual operation effect of the improved K-means algorithm,this paper classifies the players' data objects,uses the clustering analysis of NBA players' data on the basis of NBA players' statistical data,verifies the effectiveness of the improved K-means clustering algorithm,and uses JAVA as the development language.Compared with the traditional K-means clustering algorithm,the results show that the improved K-means clustering algorithm has better clustering effect than the traditional K-means clustering algorithm.By selecting the initial clustering center,the convergence of the algorithm can be effectively improved and more clustering effect can be achieved.