Discovering clusters from a dataset with different shapes, density, and scales is a known challenging problem in data clustering. In this paper, we propose the RElative COre MErge (RECOME) clustering algorithm. The core of RECOME is a novel density measure, i.e., Relative @math nearest Neighbor Kernel Density (RNKD). RECOME identifies core objects with unit RNKD, and partitions non-core objects into atom clusters by successively following higher-density neighbor relations toward core objects. Core objects and their corresponding atom clusters are then merged through @math -reachable paths on a KNN graph. Furthermore, we discover that the number of clusters computed by RECOME is a step function of the @math parameter with jump discontinuity on a small collection of values. A jump discontinuity discovery (JDD) method is proposed using a variant of the Dijkstra's algorithm. RECOME is evaluated on three synthetic datasets and six real datasets. Experimental results indicate that RECOME is able to discover clusters with different shapes, density and scales. It achieves better clustering results than established density-based clustering methods on real datasets. Moreover, JDD is shown to be effective to extract the jump discontinuity set of parameter @math for all tested dataset, which can ease the task of data exploration and parameter tuning.
@cite_4: We present a practical approach to nonparametric cluster analysis of large data sets. The number of clusters and the cluster centres are automatically derived by mode seeking with the mean shift procedure on a reduced set of points randomly selected from the data. The cluster boundaries are delineated using a k-nearest neighbour technique. The proposed algorithm is stable and efficient, a 10,000 point data set being decomposed in only a few seconds. Complex clustering examples and applications are discussed, and convergence of the gradient ascent mean shift procedure is demonstrated for arbitrary distribution and cardinality of the data.
@cite_2: Density-based clustering algorithms for multivariate data often have difficulties with high-dimensional data and clusters of very different densities. A new density-based clustering algorithm, called KNNCLUST, is presented in this paper that is able to tackle these situations. It is based on the combination of nonparametric k-nearest-neighbor (KNN) and kernel (KNN-kernel) density estimation. The KNN-kernel density estimation technique makes it possible to model clusters of different densities in high-dimensional data sets. Moreover, the number of clusters is identified automatically by the algorithm. KNNCLUST is tested using simulated data and applied to a multispectral compact airborne spectrographic imager (CASI)_image of a floodplain in the Netherlands to illustrate the characteristics of the method.
Kernel density is a well-known alternative to cut-off density. Kernel density is continuous and is less sensitive to parameter selection. However, traditional kernel density methods face the same difficulty as DBSCAN when dealing with complex datasets that contain clusters with different densities @cite_4 , and tend to confuse low-density clusters with noise. Consequently, KNN kernel density has been applied to handle such situations @cite_2 . The proposed RNKD estimation is inspired by the KNN kernel density with further improvement allowing the inclusion of low-density clusters.