摘要
聚类分析是非监督模式识别的重要分支,模糊C均值聚类算法(FCM)是其中的一类经典算法,然而该算法以一型模糊集为基础,无法处理数据集以及算法中的不确定性,为此引入区间二型模糊C均值聚类算法(IT2FCM)。二型模糊集处理不确定性的能力强于一型模糊集,基于二型模糊集的IT2FCM在处理不确定性时效果优于FCM算法。文章以图像分割为应用对象,比较IT2FCM和FCM算法的分割效果,实验证明IT2FCM较传统FCM有更好的抗噪性。
Cluster analysis is an important branch of non-supervision pattern recognition, and Fuzzy C-Means（FCM） algorithm is a classic algorithm in cluster analysis. However, FCM is founded with Type-1 fuzzy sets, which can not handle the uncertainties existing in data and algorithm itself. This paper introduces the Interval Type-2 Fuzzy C-Means（IT2FCM） algorithm, whose core is type-2 fuzzy set that has better performance on handling uncertainties than Type-1 fuzzy set. IT2FCM and FCM are used for image segmentation to compare their segmentation results. The experiment shows that IT2FCM has better performance on suppressing noise and better effects on segmenting images compared with FCM.
出处
《信息与电子工程》
2011年第6期754-758,共5页
information and electronic engineering
基金
国家自然科学基金资助项目(60674057)
中央高校专项资金资助项目(SWJTU09ZT11)
关键词
区间二型模糊集
均值聚类算法
图像分割
Interval Type-2 Fuzzy set
Fuzzy C-Means algorithm
image segmentation
