An Improved K-means Algorithm and Its Application for Assessment of Culture Industry Listed Companies

Authors

Haibo Sun, Business School of Jilin University, Changchun, Jilin, China;Economic Simulation Research Institute Jilin University of Finance and Economics Changchun, Jilin, China
Limin Wang, School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, Jilin, ChinaFollow
Xuming Han, Changchun University of Technology,College of Computer Science and Technology , Changchun, Jilin, China

Abstract

Owing to K-means algorithm has the shortcoming that it always neglects the influence of cluster size when the Euclidean distances between samples and cluster center is calculated. In order to overcome the lack, the influence of cluster size is introduced into K-means algorithm in this paper. Therefore an improved K-means algorithm based on gravity is proposed, namely GK-means algorithm. The experimental simulation results show that GK-means algorithm has better performance compared with K-means algorithm. So the GK-means algorithm is adopted for assessing the performance of culture industry listed companies in this paper. Furthermore some satisfactory results are also obtained.

Recommended Citation

Sun, Haibo; Wang, Limin; and Han, Xuming, "An Improved K-means Algorithm and Its Application for Assessment of Culture Industry Listed Companies" (2015). WHICEB 2015 Proceedings. 4.
https://aisel.aisnet.org/whiceb2015/4