Towards Parameter-Free Clustering for Real-World Data
作者：
Highlights：
• We discuss the problems of existing clustering algorithms in dealing with real-world data.
• We present a method to determine the involved parameter adaptively in our algorithm.
• By improving the density peak algorithm, our approach works well with data of Gaussian distribution and avoids cluster merging.
• In experiments on real-world data, our algorithm without parameter dependence generates comparable results to those with parameter tuning.
摘要
•We discuss the problems of existing clustering algorithms in dealing with real-world data.•We present a method to determine the involved parameter adaptively in our algorithm.•By improving the density peak algorithm, our approach works well with data of Gaussian distribution and avoids cluster merging.•In experiments on real-world data, our algorithm without parameter dependence generates comparable results to those with parameter tuning.
论文关键词：Clustering,Real-world data,Dominant set,Density peak
论文评审过程：Received 27 December 2021, Revised 24 July 2022, Accepted 20 September 2022, Available online 22 September 2022, Version of Record 3 October 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.109062
