Line graph

Algorithm

Statistics

Determining the number of clusters in a data set

DBSCAN

Canopy clustering algorithm

Theoretical computer science

Data mining

Computer science

Nearest-neighbor chain algorithm

Best bin first

k-nearest neighbors algorithm

Graph

Pattern recognition (psychology)

Mathematics

Estimator

Image (mathematics)

Dense graph

Single-linkage clustering

Artificial intelligence

CURE data clustering algorithm

Cluster analysis

1-planar graph

Correlation clustering

Noise (video)

Nearest neighbor graph

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