A new clustering algorithm based on a radar scanning strategy with applications to machine learning data
作者：
Highlights：
• The proposed algorithm discovers and extracts clusters by a radar scanning strategy.
• The number of clusters does not need to be given beforehand.
• This algorithm reduces time complexity and solves high dimensionality.
• It is capable of dealing with heterogeneous data.
• It is robust and invulnerable to noise and outliers.
摘要
•The proposed algorithm discovers and extracts clusters by a radar scanning strategy.•The number of clusters does not need to be given beforehand.•This algorithm reduces time complexity and solves high dimensionality.•It is capable of dealing with heterogeneous data.•It is robust and invulnerable to noise and outliers.
论文关键词：Adaptive clustering,Artificial intelligence,Greedy algorithm,High dimensionality,Probability density function
论文评审过程：Received 22 November 2020, Revised 5 August 2021, Accepted 21 October 2021, Available online 16 November 2021, Version of Record 12 January 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116143
