A fast DBSCAN algorithm for big data based on efficient density calculation

作者：

Highlights：

• Proposing a fast and accurate version of DBSCAN algorithm for big data.

• Calculating density based on a small subset of data called operational set.

• Creating and updating the operational set at a very low computational cost.

• Comprehensive evaluation of the proposed method in compared to other recent works.

摘要

•Proposing a fast and accurate version of DBSCAN algorithm for big data.•Calculating density based on a small subset of data called operational set.•Creating and updating the operational set at a very low computational cost.•Comprehensive evaluation of the proposed method in compared to other recent works.

论文关键词：Data Mining,Clustering,Big Data,DBSCAN Algorithm

论文评审过程：Received 27 December 2021, Revised 10 April 2022, Accepted 1 May 2022, Available online 6 May 2022, Version of Record 12 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117501