文章基本信息

标题：Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets
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作者：Seyed Mohsen Zabihi ; Mohammad-R Akbarzadeh-T
期刊名称：ISRN Artificial Intelligence
印刷版ISSN：2090-7435
电子版ISSN：2090-7443
出版年度：2012
卷号：2012
DOI：10.5402/2012/929085
出版社：Hindawi Publishing Corporation
摘要：Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant unsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy c-means (FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved versions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions FCM (GIFP-FCM) is one of them, which uses 𝐿𝑝 norm distance measure and competitive learning and outperforms the previous algorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes shadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering results by correctly eliminating most outliers during steps of clustering. We name the novel fuzzy clustering method shadowed set-based GIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the efficiency of the proposed method.