An Research Of The Clustering Algorithm Based On Intelligent Algorithm

Clustering algorithm is an effective means in analyzing the data and mining potential information.K-means clustering algorithm is a clustering algorithm based on the classification and widely used,so the improvement research of K-means clustering algorithm for improving the operation efficiency and clustering results purposes has important theoretical significance and practical application value.The K-means clustering algorithm's research background,significance and methods is summarized,and the Flower Pollination algorithm and Bat algorithm are introduced,then a research of improving K-means clustering algorithm based on Intelligent Algorithm is focused on.Firstly,the K-means clustering algorithm based on Flower Pollination algorithm is set up.In order to overcome the disadvantage of the flower pollination algorithm,such as low-accurancy computation,slow-speed convergence in later,it is improved by joining Gaussian white noise disturbance.The original clustering center of K-means algorithm is optimized by the improved flower pollination algorithm with strong global search ability.The outliers influence on clustering is weaken by the method based on distance,and the performance of the algorithm is verified and tested.The simulation results show that the algorithm effectively avoids falling into local optimum,and improves the clustering performance.Secondly,the K-means clustering algorithm based on Bat algorithm is set up.In order to solve the problem of clustering center improper selection in the traditional K-means algorithm which leads to the clustering algorithm into local optimum,the initial clustering center of K-means algorithm is searched by the bat algorithm.The simulated annealing and the niche technology based on crowding out is added into the bat algorithm,in order to overcome some problems such as slow-speed convergence in later and weak search capability,and its validity is verified by test functions.Then the initial clustering center of K-means algorithm is optimized by the improved bat algorithm.The improved algorithm is compared to the traditional K-means algorithm,and the simulation results show that the improved algorithm of clustering performance has improved greater than the traditional K-means algorithm.Thirdly,The improved K-means clustering algorithm is applied in agriculture.According the main agriculture products of Shang Dong and An Hui provinces,16 initial indexes are selected,and four comprehensive indexes are gained by using principal component analysis,then 33 different parts of the two provinces are analysed by clustering analysis,and the region is divided into two categories.The results of clustering provide the reliable basis for the managers to better understand the agricultural development present situation,improve the agricultural structure,improve the comprehensive efficiency of agriculture and speed up the economic development.Finally,the content of this article is summarized,and a further prospect in the future is made.