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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References
Zhang, G.X., et al.: Evolutionary membrane computing: a comprehensive survey and new results. Inf. Sci. 279, 528–551 (2014)
Song, B.S., Li, K.L., Zeng, X.X.: Monodirectional evolutional symport tissue p systems with promoters and cell division. IEEE Trans. Parallel Distrib. Syst. 33(2), 332–342 (2022)
Cai, Y.L., et al.: An unsupervised segmentation method based on dynamic threshold neural P systems for color images. Inf. Sci. 587, 473–484 (2022)
Dong, J.P., et al.: A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems. Inf. Sci. 596, 1–14 (2022)
Long, L.F., et al.: A time series forecasting approach based on nonlinear spiking neural systems. Int. J. Neural Syst. 32(08) (2022)
Guo, P., Jiang, W.J., Liu, Y.C.: AP system for hierarchical clustering. Int. J. Mod. Phys. C 30(8) (2019)
Jiang, Z.N., Liu, X.Y., Sun, M.H.: A density peak clustering algorithm based on the k-nearest Shannon entropy and tissue-like P system. Math. Probl. Eng. 2019 (2019)
Zhang, X.L., Liu, X.Y.: Multiview clustering of adaptive sparse representation based on coupled P systems. Entropy 24(4) (2022)
Tao, X.N., et al.: SVDD boundary and DPC clustering technique-based oversampling approach for handling imbalanced and overlapped data. Knowl.-Based Syst. 234 (2021)
Chen, J.G., et al.: A disease diagnosis and treatment recommendation system based on big data mining and cloud computing. Inf. Sci. 435, 124–149 (2018)
Precup, R.E., et al.: Evolving fuzzy models for prosthetic hand myoelectric-based control. IEEE Trans. Instrum. Meas. 69(7), 4625–4636 (2020)
Yun, U., Ryang, H., Kwon, O.C.: Monitoring vehicle outliers based on clustering technique. Appl. Soft Comput. 49, 845–860 (2016)
Wang, H., et al.: Pattern recognition and classification of two cancer cell lines by diffraction imaging at multiple pixel distances. Pattern Recogn. 61, 234–244 (2017)
Lei, T., et al.: Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans. Fuzzy Syst. 26(5), 3027–3041 (2018)
Giacoumidis, E., et al.: Blind nonlinearity equalization by machine-learning-based clustering for single- and multichannel coherent optical OFDM. J. Lightwave Technol. 36(3), 721–727 (2018)
Gowanlock, M., et al.: A hybrid approach for optimizing parallel clustering throughput using the GPU. IEEE Trans. Parallel Distrib. Syst. 30(4), 766–777 (2019)
Singh, S.K., Kumar, P., Singh, J.P.: An energy efficient protocol to mitigate hot spot problem using unequal clustering in WSN. Wirel. Pers. Commun. 101(2), 799–827 (2018). https://doi.org/10.1007/s11277-018-5716-3
Chen, T., et al.: Model-based multidimensional clustering of categorical data. Artif. Intell. 176(1), 2246–2269 (2012)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Du, M.J., Ding, S.F., Jia, H.J.: Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowl.-Based Syst. 99, 135–145 (2016)
Zhao, J., et al.: Density peaks clustering algorithm based on fuzzy and weighted shared neighbor for uneven density datasets. Pattern Recogn. 139 (2023)
Lotfi, A., Moradi, P., Beigy, H.: Density peaks clustering based on density backbone and fuzzy neighborhood. Pattern Recogn. 107 (2020)
Peng, H., et al.: An automatic clustering algorithm inspired by membrane computing. Pattern Recogn. Lett. 68, 34–40 (2015)
Zhu, X.: Semi-supervised learning with graphs. Doctoral Dissertation. Carnegie Mellon University, CMU–LTI–05–192 (2005)
Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. KDD 96, 226–231 (1996)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. Stat. Probab. 281–297 (1967)
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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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