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Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets

机译：具有改进的模糊分区和影子集的广义模糊C均值聚类

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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 usesLpnorm 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.
机译：聚类涉及根据某种相似性度量将数据点分组在一起。聚类是最重要的无监督学习问题之一，不需要任何标记数据。聚类算法很多，其中模糊c均值（FCM）是最流行的方法之一。 FCM具有基于欧几里得距离的目标函数。近年来，人们提出了一些功能有所不同的FCM改进版本。广义改进模糊分区FCM（GIFP-FCM）就是其中之一，它使用Lpnorm距离度量和竞争性学习，并且胜过了该领域以前的算法。在本文中，我们提出了一种新的具有改进的模糊分区的FCM聚类方法，该方法利用了阴影集，并尝试改进嘈杂数据集中的GIFP-FCM。通过正确消除聚类步骤中的大多数异常值，它可以提高GIFP-FCM的效率并改善聚类结果。我们将新颖的模糊聚类方法命名为基于阴影集的GIFP-FCM（SGIFP-FCM）。在DRIVE数据库的视网膜图像中进行血管分割的一些实验证明了该方法的有效性。

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