KNNAC

Abstract

Density-based clustering algorithms are commonly adopted when arbitrarily shaped clusters exist. Usually, they do not need to know the number of clusters in prior, which is a big advantage. Conventional density-based approaches such as DBSCAN, utilize two parameters to define density. Recently, novel density-based clustering algorithms are proposed to reduce the problem complexity to the use of a single parameter k by utilizing the concepts of k Nearest Neighbor (kNN) and Reverse k Nearest Neighbor (RkNN) to define density. However, those kNN-based approaches are either ineffective or inefficient. In this paper, we present a new clustering algorithm KNNAC, which only requires computing the densities for a chosen subset of points due to the use of active core detection. We empirically show that, compared to other nearest neighbor based clustering approaches (e.g., RECORD, IS-DBSCAN, etc.), KNNAC can provide competitive performance while taking a fraction of the runtime.