Application of Fuzzy C-means clustering algorithm in image segmentation
Available Online December 2016.
- https://doi.org/10.2991/iceeecs-16.2016.18How to use a DOI?
- Interval type-2 fuzzy sets; means clustering algorithm; Image Segmentation.
- Data mining was a core process step in the whole data processing system. The aim is to use specific data mining algorithms to extract knowledge of interest. The user from the database and represented in a certain way, such as the generation rules. Typically, data mining algorithms can be implemented features include: class concept descriptions, association rules, classification regression, cluster analysis, sequence timing analysis, and isolated point analysis. This paper describes one of the most common algorithms based on fuzzy clustering algorithm, which called FCM objective function. FCM algorithm discussed two types of improvement ideas: First, the introduction of clustering validity function in FCM algorithm process to determine the number of clusters c. Second is the evolutionary computation is introduced into the FCM algorithm. On this basis, an improved FCM algorithm, namely through the introduction of simulated annealing particle swarm algorithm FCM is improved to reduce the impact of isolated points on the cluster center.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Rongchuan Guo PY - 2016/12 DA - 2016/12 TI - Application of Fuzzy C-means clustering algorithm in image segmentation BT - 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/iceeecs-16.2016.18 DO - https://doi.org/10.2991/iceeecs-16.2016.18 ID - Guo2016/12 ER -