AI Article Synopsis
- The DPC algorithm is effective for sample distribution and noise point identification but lacks adaptability and struggles with high time complexity.
- This study introduces improvements to DPC using maximum nearest neighbor distance and K-nearest neighbors, and proposes a new method based on delayed spiking neural P systems (DSN P systems).
- The new DSNP-ANDPC algorithm demonstrates superior performance in most cases when evaluated against various synthetic and real-world datasets.
Article Abstract
Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.
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| http://dx.doi.org/10.1142/S0129065724500503 | DOI Listing |
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