Fuzzy Neighborhood Grid-Based DBSCAN Using Representative Points

Clustering process is considered as one of the most important part in data mining, and it passes through many levels of developments. One of the most famous algorithm is Density-Based Spatial Clustering of Application with Noise (DBSCAN) [1,2,4]. It is a density-based clustering algorithm that uses a crisp neighborhood function to calculate the neighbor sets, and basically depends on distance function. In fuzzy clustering [9], which is considered as a soft clustering algorithm, it uses a fuzzy neighborhood function that allow a node in the dataset to have a membership degree in each point in the dataset. In this paper we propose a new algorithm that depends on both bases the speed of DBSCAN and the accuracy of fuzzy clustering. FNGMDBSCAN-UR is a Fuzzy Neighborhood Gridbased Multi-density DBSCAN Using Representative points. That uses grid-based to separate the dataset into small nets and fuzzy neighborhood function to create neighborhood sets.it is noticeable that FNGMDBSCAN-UR is much accurate than crisp DBSCAN with nested shapes and multi-dense datasets as we will see in the result section in this paper.