An Improved Density-Based Spatial Clustering of Applications with Noise Algorithm with an Adaptive Parameter Based on the Sparrow Search Algorithm

An Improved Density-Based Spatial Clustering of Applications with Noise Algorithm with an Adaptive Parameter Based on the Sparrow Search Algorithm

The density-based spatial clustering of applications with noise (DBSCAN) is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly...

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Bibliographic Details
Main Authors: Zicheng Huang, Zuopeng Liang, Shibo Zhou, Shuntao Zhang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Algorithms
Subjects:
DBSACN algorithm
adaptive parameter
sparrow optimization algorithm
data mining
Online Access:https://www.mdpi.com/1999-4893/18/5/273
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