Nearest-neighbor chain algorithm

1-planar graph

Canopy clustering algorithm

Statistics

k-nearest neighbors algorithm

Algorithm

Image (mathematics)

Data mining

Dense graph

Graph

Determining the number of clusters in a data set

Pattern recognition (psychology)

Artificial intelligence

Estimator

Line graph

CURE data clustering algorithm

Mathematics

Computer science

DBSCAN

Cluster analysis

Theoretical computer science

Correlation clustering

Noise (video)

Pattern Recognition

Density-based clustering has several desirable properties, such as the abilities to handle and identify noise samples, discover clusters of arbitrary shapes, and automatically discover of the number of clusters. Identifying the core samples within the dense regions of a dataset is a significant step of the density-based clustering algorithm. Unlike many other algorithms that estimate the density of each samples using different kinds of density estimators and then choose core samples based on a threshold, in this paper, we present a novel approach for identifying local high-density samples utilizing the inherent properties of the nearest neighbor graph (NNG). After using the density estimator to filter noise samples, the proposed algorithm ADBSCAN in which “A” stands for “Adaptive” performs a DBSCAN-like clustering process. The experimental results on artificial and real-world datasets have demonstrated the significant performance improvement over existing density-based clustering algorithms.

A novel density-based clustering algorithm using nearest neighbor graph