Abstract
The density peak clustering (DPC) proposed in 2014 has attracted extensive discussion and research. The DPC algorithm considers the connectivity of objects from the perspective of object density and continuously expands clusters based on connectivity to obtain the final clustering results. However, the DPC algorithm also has its drawbacks. The DPC algorithm requires appropriate as of distance parameter \({d}_{c}\) for different datasets. DPC is prone to chain reactions after an object misclassification. This paper proposes a new method called dynamic label propagation density peak clustering based on the tissue-like P systems (TP-DLDPC). The entire method operates within the frame construction of the tissue-like P systems. Firstly, the local density is calculated using a fuzzy kernel function to reduce the parameter sensitivity of the method. Secondly, object assignment is completed by multiple iterations using a dynamic label propagation assignment strategy. Comparative experiments are carried out on seven datasets, and the consequences show that the proposed method has a good clustering performance.
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Acknowledgment
This study is supported by the Social Science Fund Project of Shandong (16BGLJ06, 11CGLJ22).
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Du, Q., Liu, X. (2023). Dynamic Label Propagation Density Peak Clustering Based on the Tissue-Like P Systems. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_11
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DOI: https://doi.org/10.1007/978-981-99-4752-2_11
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