Mining Maximal High Utility Co-location Patterns from Large-Scale Spatial Data
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0844-4_13
Reference16 articles.
1. Bao, X., Wang, L.: A clique-based approach for co-location pattern mining. Inf. Sci. 490, 244–264 (2019)
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3. Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in sparse graphs in near-optimal time. In: Cheong, O., Chwa, K.Y., Park, K. (eds.) Algorithms and Computation. ISAAC 2010. LNCS, vol. 6506 pp. 403–414. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17517-6_36
4. Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in large sparse real-world graphs. J. Exp. Algorithmics (JEA) 18, 3–1 (2013)
5. Li, Y., Wang, L., Yang, P., Li, J.: EHUCM: an efficient algorithm for mining high utility co-location patterns from spatial datasets with feature-specific utilities. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) Database and Expert Systems Applications. DEXA 2021. LNCS, vol. 12923, pp. 185–191. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86472-9_17
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