Abstract
Clustering has been widely used in visual analysis, pattern recognition, privacy protection and other fields. In recent years, numerous clustering methods have received increasing attention. However, discovering arbitrarily shaped clusters, determining the location and number of clustering cores and dealing with fuzzy boundaries is tough for most algorithms. We propose a novel clustering algorithm with dynamic boundary extraction strategy based on local gravitation (DBELG) which extracts boundary in a natural way, rather than mechanically defining a few core points. In order to identify fuzzy boundaries, a novel gravity model that makes use of three significant information about the data objects is proposed. The structure of the reserved core groups is clear and easy to cluster. On this basis, the core group clustering (CGC) is further proposed to cluster the core points. The experimental results show that DBELG achieves better performance than existing methods in handling datasets with fuzzy boundaries and complex structures.
Supported by grants from the Graduate Scientific Research and Innovation Foundation of Chongqing, China (No. CYB20063), and the National Natural Science Foundation of China (No. 62006029).
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References
Wang, R., Fung, B.C., Zhu, Y.: Heterogeneous data release for cluster analysis with differential privacy. Knowl. Based Syst. 201, 106047 (2020)
AlMahmoud, R.H., Hammo, B., Faris, H.: A modified bond energy algorithm with fuzzy merging and its application to Arabic text document clustering. Exp. Syst. Appl. 159, 113598 (2020)
Hu, F., Chen, H., Wang, X.: An intuitionistic kernel-based fuzzy c-means clustering algorithm with local information for power equipment image segmentation. IEEE Access 8, 4500–4514 (2020)
Bose, A., Mali, K.: Type-reduced vague possibilistic fuzzy clustering for medical images. Pattern Recogn. 112, 107784 (2021)
Bu, F., Hu, C., Zhang, Q., Bai, C., Yang, L.T., Baker, T.: A cloud-edge-aided incremental high-order possibilistic c-means algorithm for medical data clustering. IEEE Trans. Fuzzy Syst. 29(1), 148–155 (2020)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Sheng, W., Liu, X.: A genetic k-medoids clustering algorithm. J. Heuristics 12(6), 447–466 (2006)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)
Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160–172. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_14
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Liu, R., Wang, H., Yu, X.: Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Inf. Sci. 450, 200–226 (2018)
Chen, Y.: Decentralized clustering by finding loose and distributed density cores. Inf. Sci. 433, 510–526 (2018)
Averbuch-Elor, H., Bar, N., Cohen-Or, D.: Border-peeling clustering. IEEE Trans. Pattern Anal. Mach. Intell. 42(7), 1791–1797 (2019)
Yuan, M., Zhu, Q.: Spectral clustering algorithm based on fast search of natural neighbors. IEEE Access 8, 67277–67288 (2020)
Shi, J., Zhu, Q., Li, J., Liu, J., Cheng, D.: Hierarchical clustering based on local cores and sharing concept. In: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 284–289. IEEE (2021)
Zhu, Q., Feng, J., Huang, J.: Natural neighbor: a self-adaptive neighborhood method without parameter k. Pattern Recogn. Lett. 80, 30–36 (2016)
Srinilta, C., Kanharattanachai, S.: Application of natural neighbor-based algorithm on oversampling smote algorithms. In: 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp. 217–220. IEEE (2021)
Wu, Z., Zeng, Y., Li, D., Liu, J., Feng, L.: High-volume point cloud data simplification based on decomposed graph filtering. Autom. Constr. 129, 103815 (2021)
Dua, D., Graff, C., et al.: UCI machine learning repository (2017)
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Luo, J., Zhu, Q., Li, J., Cheng, D., Zhou, M. (2022). A Novel Clustering Algorithm with Dynamic Boundary Extraction Strategy Based on Local Gravitation. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13281. Springer, Cham. https://doi.org/10.1007/978-3-031-05936-0_14
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