.png){kind=icon}
Return
A simple statistics-based nearest neighbor cluster detection algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2014.10.009.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Clusters
Cluster detection
Clustering
Cluster analysis
Digital geometry
Nearest neighbors
Neighborhoods
Statistics
Pattern recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveTinnitus Retraining Therapy (TRT) as a Method for Treatment of Tinnitus and Hyperacusis Patients
PREAI
ShareSaveShareSaveShareSaveShareSaveSeparation by dielectrophoresis of dormant and nondormant bacterial cells of Mycobacterium smegmatis
OAAI
OAAIShareSavePsychological characteristics and biofeedback mitigation in preadolescents with eating disorders
PREAI
ShareSaveShareSave
View more