Author:
Xiong Meiyu,Chen Hongmei,Wang Lizhen,Xiao Qing
Publisher
Springer Nature Singapore
Reference10 articles.
1. Wang, L., Bao, X., Zhou, L., et al.: Maximal sub-prevalent co-location patterns and efficient mining algorithms. In: Bouguettaya, A., et al. (eds.) Web Information Systems Engineering – WISE 2017. WISE 2017. LNCS, vol. 10569, pp. 199–214. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68783-4_14
2. Wang, L., Bao, X., Zhou, L., et al.: Mining maximal co-location patterns. World Wide Web 22(5), 1971–1997 (2019)
3. Yang, S., Wang, L., Bao, X., Lu, J.: A framework for mining spatial high utility co-location patterns. In: FSKD 2015, Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 595–601. IEEE, Zhangjiajie, China (2015)
4. Jiang, X., Wang, L., Tran, V.: A parallel algorithm for regional co-location mining based on fuzzy density peak clustering. Sin Sci. Inform. 53(7), 1281–1298 (2023)
5. 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)