Clustering is one of the unsupervised learning problems. It is a procedure which partitions data objects into groups. Many algorithms could not overcome the problems of morphology, overlapping and the large number of clusters at the same time. Many scientific communities have used the clustering algorithm from the perspective of density, which is one of the best methods in clustering. This study proposes a density-based spatial clustering of applications with noise (DBSCAN) algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN (AFD) which works with the initialization of two parameters. AFD, by using fuzzy and DBSCAN features, is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically. The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset. The model overcomes the problems of clustering such as morphology, overlapping, and the number of clusters in a dataset simultaneously. In the experiments, all algorithms are performed on eight data sets with 30 times of running. Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets. It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.

中文翻译：

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形态和重叠数据集的自动Fuzzy-DBSCAN算法

聚类是无人监督的学习问题之一。这是将数据对象分为几组的过程。许多算法无法同时克服形态，重叠和大量簇的问题。从密度的角度来看，许多科学团体都使用了聚类算法，这是聚类的最佳方法之一。这项研究提出了一种基于噪声的应用程序的基于密度的空间聚类（DBSCAN）算法，该算法基于选定的高密度区域，通过自动模糊DBSCAN（AFD）来处理两个参数的初始化。通过使用模糊和D​​BSCAN功能，可以通过选择高密度区域对AFD进行建模，并生成两个用于自动合并和分离的参数。生成的两个参数在笛卡尔坐标系中为数据集提供了子集群规则的状态。该模型克服了聚类的问题，例如形态，重叠以及同时在数据集中聚类的数量。在实验中，所有算法都对八个数据集执行了30次运行。其中三个与重叠的真实数据集有关，其余的与形态和合成数据集有关。结果表明，AFD算法优于其他最近开发的聚类算法。其中三个与重叠的真实数据集有关，其余的与形态和合成数据集有关。证明了AFD算法优于其他最近开发的聚类算法。其中三个与重叠的真实数据集有关，其余的与形态和合成数据集有关。结果表明，AFD算法优于其他最近开发的聚类算法。