Abstract
Existing clustering methods rely on prior knowledge of the data set to cluster it, so the quality of the clustering effect is entirely dependent on the user’s familiarity with the data set. Furthermore, when extracting the information from a data set, existing clustering algorithms frequently ignore the geometric distribution of data, making it difficult to identify data objects in their entirety and detect local spatial structures. To address these issues, this paper proposes a spatial subcluster clustering method by grid-connection, which automatically obtains subclusters by iterative local labeling without requiring a priori knowledge of the data set and efficiently extracts correlations between data by establishing relationships between subclusters by grid-connecting. Experiments are conducted to validate the proposed algorithm against existing state-of-the-art algorithms on 9 synthetic and 4 real data sets. The results show SSCG can efficiently utilize the information on the grid space without relying on a priori knowledge, and the overall performance is better than the existing advanced algorithms.
Y. Zhang and X. Han—Equally contributed to this work
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Zhang, Y., Han, X., Wang, L., Chen, W., Guo, L. (2023). SSCG: Spatial Subcluster Clustering Method by Grid-Connection. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_32
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