Sensitivity Analysis and Optimization of FCM Initial Clustering Centers
- 10.2991/sschd-17.2017.97How to use a DOI?
- fuzzy C- means clustering, sensitivity analysis, clustering centers move with fuzzy exponent
Some researches show that fuzzy C- means clustering is sensitive to initial clustering centers. Related variables include cluster number, data characteristics and fuzzy exponent are affect the sensitivity of the algorithm. This paper provided the definition of the sensitivity of the algorithm. Selecting data sets of different characteristics, experimental analyzed the sensitivity of the algorithm to the initial clustering centers under different clustering case, and compared the convergence speed of different initial clustering centers reach the specific clustering distance. The results show that the algorithm is not absolutely sensitive to the initial clustering centers, the sensitivity is related to the number of clustering for the same data set. This paper proposed the clustering centers move with fuzzy exponent algorithm for FCM algorithm by combining the maximum distances product method, and anlyzed the validity of the optimization algorithm.
- © 2017, 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 - Yong Wang AU - Jiao Cheng AU - Ling Zhang AU - Qiu-Hua Tang PY - 2017/09 DA - 2017/09 TI - Sensitivity Analysis and Optimization of FCM Initial Clustering Centers BT - Proceedings of the 3rd Annual International Conference on Social Science and Contemporary Humanity Development PB - Atlantis Press SP - 506 EP - 513 SN - 2352-5398 UR - https://doi.org/10.2991/sschd-17.2017.97 DO - 10.2991/sschd-17.2017.97 ID - Wang2017/09 ER -