Landmark FN-DBSCAN: An Efficient Density-Based Clustering Algorithm with Fuzzy Neighborhood

However, in general, the parameters of density-based clustering algorithms are usually difficult to select. So, in order to make the density-based clustering algorithms more robust, the extension with fuzzy set theory has attracted a lot of attentions recently. The fuzzy neighborhood DBSCAN (FNDBSCAN) is a typical one with this idea. But FN-DBSCAN usually requires a time complexity of O(n2) where n is the number of data in the data set. This implies that the algorithm is not suitable for the work with large scale data sets.