Abstract
Prevalent co-location patterns (PCPs) expose the latent relationships between spatial instances and features on spatial data. Traditional PCPs treat spatial instances and features equally. However, in real life, spatial instances and features have different importance depending on their significance or meanings, this leads to traditional PCPs may lose their usefulness and meaningfulness. To address this deficiency, prevalent weighted co-location patterns (PWCPs) are proposed. In PWCP mining, each spatial instance is considered with its varied importance as its weight. The weight of a co-location pattern is determined by the weights of spatial features and instances involved in the pattern. Unfortunately, the weight metric does not satisfy the downward closure property, mining PWCPs becomes very expensive since unnecessary candidates cannot be pruned. To tackle this, an efficient PWCP mining framework is developed based on maximal cliques. This framework improves mining performance by reducing the searching PWCP candidates efficiently. The effectiveness and efficiency of the proposed method are proved by the mining results on both spatial synthetic and real data sets.
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References
Andrzejewski, W., Boinski, P.: Parallel gpu-based plane-sweep algorithm for construction of icpi-trees. J. Database Manag. 26(3), 1–20 (2015)
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.) ISAAC 2010. LNCS, vol. 6506, pp. 403–414. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17517-6_36
He, Z., Deng, M., Xie, Z., Wu, L., Chen, Z., Pei, T.: Discovering the joint influence of urban facilities on crime occurrence using spatial co-location pattern mining. Cities 99, 102612 (2020)
Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. IEEE Trans. Knowl. Data Eng. 16(12), 1472–1485 (2004)
Li, J., Adilmagambetov, A., Mohomed Jabbar, M.S., Zaïane, O.R., Osornio-Vargas, A., Wine, O.: On discovering co-location patterns in datasets: a case study of pollutants and child cancers. GeoInformatica 20(4), 651–692 (2016)
Sheshikala, M., Rao, R.D.: A map-reduce framework for finding clusters of colocation patterns-a summary of results. In: IACC, pp. 129–131. IEEE (2017)
Tran, V., Wang, L., Chen, H., Xiao, Q.: Mcht: a maximal clique and hash table-based maximal prevalent co-location pattern mining algorithm. Expert Syst. Appl. 175, 114830 (2021)
Tran, V., Wang, L., Zhou, L.: A spatial co-location pattern mining framework insensitive to prevalence thresholds based on overlapping cliques. In: Distributed and Parallel Databases, pp. 1–38 (2021)
Wang, L., Bao, X., Zhou, L.: Redundancy reduction for prevalent co-location patterns. IEEE Trans. Knowl. Data Eng. 30(1), 142–155 (2017)
Wang, L., Jiang, W., Chen, H., Fang, Y.: Efficiently mining high utility co-location patterns from spatial data sets with instance-specific utilities. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10178, pp. 458–474. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55699-4_28
Yao, X., Jiang, X., Wang, D., Yang, L., Peng, L., Chi, T.: Efficiently mining maximal co-locations in a spatial continuous field under directed road networks. Inf. Sci. 542, 357–379 (2021)
Yoo, J.S., Boulware, D., Kimmey, D.: Parallel co-location mining with mapreduce and nosql systems. Knowl. Inf. Syst. 62(4), 1433–1463 (2020)
Yoo, J.S., Bow, M.: A framework for generating condensed co-location sets from spatial databases. Intell. Data Anal. 23(2), 333–355 (2019)
Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323–1337 (2006)
Yu, W.: Spatial co-location pattern mining for location-based services in road networks. Expert Syst. Appl. 46, 324–335 (2016)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (61966036), the Project of Innovative Research Team of Yunnan Province (2018HC019), the Yunnan Fundamental Research Projects (202201AS070015), and the Program for Young and Middle-aged Academic and Technical Reserve Leaders of Yunnan Province (202205AC160033).
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Tran, V., Wang, L., Zou, M., Chen, H. (2023). Discovering Prevalent Weighted Co-Location Patterns on Spatial Data Without Candidates. 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 13421. Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_33
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