Density peaks clustering: a gentle comparison

Haozheng Yang

doi:10.1117/12.2636665

6 May 2022 Density peaks clustering: a gentle comparison

Haozheng Yang

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217615 (2022) https://doi.org/10.1117/12.2636665
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Density peaks clustering (DPC) is a new algorithm based on density in clustering analysis. The algorithm considers local density and relative distance to draw the decision graph, quickly identify cluster centers, and complete clustering. The DPC has a unique input parameter and requires no prior knowledge or iteration. Since it was proposed in 2014, DPC has aroused great interest among scholars and developed rapidly. The paper first introduces the advantages and disadvantages of DPC and its basic theory. Secondly, we analyze the DPC optimization method from four perspectives. Then, five improved algorithms of DPC are introduced, and experiments on DPC and these improved algorithms are carried out in synthetic datasets and real-world datasets to evaluate the performance of these algorithms.

Citation Download Citation

Haozheng Yang "Density peaks clustering: a gentle comparison", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217615 (6 May 2022); https://doi.org/10.1117/12.2636665

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