An IMPROVED FCM-POSSIBLE CLUSTERING ALGORITHM FOR INTERVAL DATA
- DOI
- 10.2991/isci-15.2015.177How to use a DOI?
- Keywords
- Interval data; Improved FCM-Possible Clustering Algorithm; Average CR index
- Abstract
In order to overcome the disadvantages of Fuzzy c-Means(FCM) and possible clustering algorithm in the practical application of interval data, an improved FCM-possible clustering algorithm for interval data is proposed in this paper by combing their merits. The improved clustering algorithm introduce the possibility theory into the clustering problem of interval data, by relaxing the constraints of the sample membership and modifying IFCM algorithm's objective function The results of simulation experiments and the average CR index analysis show that: For the cluster problems containing poorly representative sample data such as noise and outliers, the improved FCM-possible clustering algorithm proposed in this paper is much better than the FCM algorithm and possible clustering algorithm, which can effectively reduce the influence on the clustering result by the noise .
- Copyright
- © 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 - Qing Li AU - Jianlu Luo AU - Xiaodong Tan AU - Xiaoyan Deng AU - Bing Lu PY - 2015/01 DA - 2015/01 TI - An IMPROVED FCM-POSSIBLE CLUSTERING ALGORITHM FOR INTERVAL DATA BT - Proceedings of the 2015 International Symposium on Computers & Informatics PB - Atlantis Press SP - 1332 EP - 1338 SN - 2352-538X UR - https://doi.org/10.2991/isci-15.2015.177 DO - 10.2991/isci-15.2015.177 ID - Li2015/01 ER -