Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant\r\nunsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy cmeans\r\n(FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved\r\nversions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions\r\nFCM (GIFP-FCM) is one of them, which uses Lp norm distance measure and competitive learning and outperforms the previous\r\nalgorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes\r\nshadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering\r\nresults by correctly eliminating most outliers during steps of clustering.We name the novel fuzzy clustering method shadowed setbased\r\nGIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the\r\nefficiency of the proposed method.
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