Abstract
Co-location pattern mining aims to discover the relationships between spatial features. Traditional co-location patterns are based on clique relationships and only consider the prevalence of patterns. However, pollution sources and cancer cases do not satisfy the clique relationship, and users focus on the influence of pollution sources on cancer cases. Therefore, we propose high-influence ordered-pair patterns to study their relationships. First, we measure the influence of pollution sources on cancer cases. Then, to efficiently mine high-influence ordered-pair patterns, we propose a basic algorithm with two pruning strategies and an optimizing algorithm based on participating instances. Extensive experiments on real and synthetic datasets show that our mining results are more reasonable than existing algorithms and can provide guidance for cancer prevention. Moreover, our algorithm is also highly efficient and scalable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yao, X., Peng, L., Yang, L., Chi, T.: A fast space-saving algorithm for maximal co-location pattern mining. Expert Syst. Appl. 63(C), 310–323 (2016)
Bao, X., Wang, L.: A clique-based approach for co-location pattern mining. Inf. Sci. 490, 244–264 (2019)
Bing, Z., Zuqiang, M., Liangliang, S., Hongli, L.I., Computer, S.O.: Adaptive k neighbor algorithm based on local density and purity. J. Guangxi Acad. Sci. 33(1), 19–24 (2017)
García-Pérez, J., et al.: Residential proximity to industrial pollution sources and colorectal cancer risk: a multicase-control study (MCC-Spain). Environ. Int. 144, 106055 (2020)
Lei, L., Wang, L., Zeng, Y., Zeng, L.: Discovering high influence co-location patterns from spatial data sets. In: ICBK, pp. 137–144. IEEE (2019)
Ramis, R., Diggle, P., Cambra, K., López-Abente, G.: Prostate cancer and industrial pollution. Environ. Int. 37(3), 577–585 (2011)
Terrell, K.A., St Julien, G.: Air pollution is linked to higher cancer rates among black or impoverished communities in Louisiana. Environ. Res. Lett. 17(1), 014033 (2022)
Wang, L., Zhou, L., Lu, J., Yip, J.: An order-clique-based approach for mining maximal co-locations. Inf. Sci. 179(19), 3370–3382 (2009)
Yang, P., Wang, L., Wang, X., Zhou, L.: SCPM-CR: a novel method for spatial co-location pattern mining with coupling relation consideration. IEEE Trans. Knowl. Data Eng. (2021). https://doi.org/10.1109/TKDE.2021.3060119
Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323–1337 (2006)
Zambon, P., et al.: Sarcoma risk and dioxin emissions from incinerators and industrial plants: a population-based case-control study (Italy). Environ. Health 6(1), 19 (2007)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (61966036, 62062066), and Yunnan University Postgraduate Technological Innovation Project(2021Y175).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shu, J., Wang, L., Yang, P., Tran, V. (2022). Mining the Potential Relationships Between Cancer Cases and Industrial Pollution Based on High-Influence Ordered-Pair Patterns. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-22064-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22063-0
Online ISBN: 978-3-031-22064-7
eBook Packages: Computer ScienceComputer Science (R0)