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.
DBSCAN @cite_14 is one of the most classic density-based methods. In DBSCAN, the cut-off density of an object @math is defined as the number of objects falling inside a ball of radius @math centred at @math . If the cut-off density of @math is higher than @math , @math is regarded as a key object. When the distance between two key objects is less than @math , they are called density-reachable. Density-reachable key objects form basic clusters. A non-key object is assigned to a basic cluster if it is within @math distance to a key object in the respective cluster; otherwise, the non-key object is treated as noise. In DBSCAN, @math and @math need to be carefully tuned as they would affect the number and formations of the final clusters. Furthermore, DBSCAN cannot handle clusters of heterogeneous densities well as both parameters, even properly selected, are fixed. In contrast, in this work, we introduce the novel concept of RNKD, which in effect homogenizes density measures and thus can handle clusters of different densities.
@cite_14
2161806858
Clustering is one of the basic data mining tasks that can be used to extract hidden information from data in the absence of target classes. One of the most well-known density based clustering algorithms for processing spatial data is Density-Based Spatial Clustering of Application with Noise (DBSCAN) that uses learning parameters epsiv and minPts to define the density that will be sought in the data set while forming the clusters. The major drawbacks of the DBSCAN algorithm are its sensitivity to user input required to execute the algorithm, inability to recognize clusters with different densities, and computational complexity. In this study, we propose a three-level clustering method to address the second issue. The first level selects appropriate grids so that the density is homogeneous in each grid. The second stage merges cells with similar densities and identifies the most suitable values of epsiv and minPts in each grid that remain after merging. The third step of the proposed method executes the DBSCAN method with these identified parameters in the dataset. The proposed method is tested in three artificial benchmark data sets to demonstrate that the clusters are correctly identified.