K-means Algorithm Based on Fitting Function
- 10.2991/asei-15.2015.383How to use a DOI?
- Density,Optimal distance, Fitting function,K-means
The K-means algorithm has the shortcomings of being sensitive to the initial clustering center, and in order to overcome this drawback, in this paper ,on the basis of the combination of data density and the optimal distance , a new definition of fitting function is made and then a kind of K-means algorithm based on fitting function is proposed. By utilizing the fitting function to select the initial clustering center, the selection of the initial cluster centers can be made as much close to the real sample clustering centers as possible. The experiments proved that, the K-means algorithm based on fitting function reduces the number of iterations and enhances the stability of the algorithm, as well as improves the efficiency of the algorithm.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - SiYong Chu AU - YanNi Deng AU - LinLi Tu PY - 2015/05 DA - 2015/05 TI - K-means Algorithm Based on Fitting Function BT - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation PB - Atlantis Press SP - 1940 EP - 1945 SN - 2352-5401 UR - https://doi.org/10.2991/asei-15.2015.383 DO - 10.2991/asei-15.2015.383 ID - Chu2015/05 ER -