Application of Fuzzy C-means clustering algorithm in image segmentation

DOI

10.2991/iceeecs-16.2016.18How to use a DOI?

Keywords

Interval type-2 fuzzy sets; means clustering algorithm; Image Segmentation.

Abstract

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.

Copyright

© 2016, 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/).

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TY  - CONF
AU  - Rongchuan Guo
PY  - 2016/12
DA  - 2016/12
TI  - Application of Fuzzy C-means clustering algorithm in image segmentation
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 84
EP  - 88
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.18
DO  - 10.2991/iceeecs-16.2016.18
ID  - Guo2016/12
ER  -