A simple statistics-based nearest neighbor cluster detection algorithm. Issue 3 (March 2015)

• Record Type: Journal Article
• Authors: Ritter, Gerhard X.; Nieves-Vázquez, José-A.; Urcid, Gonzalo
• Is Part Of: Pattern recognition. Volume 48:Issue 3(2015:Mar.)
• Page Start: 918
• Page End: 932
• Publication Date: 2015-03
• Subjects: Clusters -- Cluster detection -- Clustering -- Cluster analysis -- Digital geometry -- Nearest neighbors -- Neighborhoods -- Statistics -- Pattern recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2014.10.009
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: We propose a new method for autonomously finding clusters in spatial data. The proposed method belongs to the so called nearest neighbor approaches for finding clusters. It is a repetitive technique which produces changing averages and deviations of nearest neighbor distance parameters and results in a final set of clusters. The proposed technique is capable of eliminating background noise, outliers, and detection of clusters with different densities in a given data set. Using a wide variety of data sets, we demonstrate that the proposed cluster seeking algorithm performs at least as well as various other currently popular algorithms and in several cases surpasses them in performance. Abstract : Highlights: A new clustering algorithm based on simple statistics and lattice metrics is given. Mathematical rationale is explained in detail and theorem proofs are provided. Performance classification of the SSNN algorithm is illustrated with 2D datasets. Jain׳s benchmark dataset is used to show the SSNN cluster finding capability. High-dimensional image patterns are included as additional clustering examples.