Abstract
Discovering prevalent co-location patterns (PCPs) is a process of finding a set of spatial features in which their instances frequently occur in close geographic proximity to each other. Most of the existing algorithms collect co-location instances to evaluate the prevalence of spatial co-location patterns, that is if the participation index (a prevalence measure) of a pattern is not smaller than a minimum prevalence threshold, the pattern is a PCP. However, collecting co-location instances is the most expensive step in these algorithms. In addition, if users change the minimum prevalence threshold, they have to re-collect all co-location instances for obtaining new results. In this paper, we propose a new prevalent co-location pattern mining framework that does not need to collect co-location instances of patterns. First, under a distance threshold, all cliques of an input dataset are enumerated. Then, a co-location hashmap structure is designed to compact all these cliques. Finally, participation indexes of patterns are efficiently calculated by the co-location hashmap structure. To demonstrate the performance of the proposed framework, a set of comparisons with the previous algorithm which is based on collecting co-location instances on both synthetic and real datasets is made. The comparison results indicate that the proposed framework shows better performance.
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References
Yu, W.: Spatial co-location pattern mining for location-based services in road networks. Expert Syst. Appl. 46, 324–335 (2016)
Akbari, M., Samadzadegan, F., Weibel, R.: A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution. J Geogr Syst. 17, 249–274 (2015)
Mohan, P., Shekhar, S., Shine, J.: A neighborhood graph based approach to regional co-location pattern discovery: a summary of results. In: 19th ACM SIGSPATIAL, pp. 122–132. ACM, NY (2011)
Cai, J., Deng, M., Liu, Q.: Nonparametric significance test for discovery of network-constrained spatial co-location patterns. Geogr. Anal. 51, 3–22 (2019)
Deng, M., He, Z., Liu, Q.: Multi-scale approach to mining significant spatial co-location patterns. Trans. GIS 21, 1023–1039 (2017)
Wang, S., Huang, Y., Wang, X.: Regional co-locations of arbitrary shapes. In: Advances in Spatial and Temporal Databases, pp. 19–37. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-40235-7_2
Kishor, P., Porika, S.: An efficient approach for mining positive and negative association rules from large transactional databases. In: ICICT, pp. 1–5. IEEE, India (2016)
Shekhar, S., Huang, Y.: Discovering spatial co-location patterns: a summary of results. In: Advances in Spatial and Temporal Databases, pp. 236–256. Springer, Berlin (2001). https://doi.org/10.1007/3-540-47724-1_13
Yoo, J., Shekhar, S., Smith, J., Kumquat, J.: A partial join approach for mining co-location patterns. In: 12th Annual ACM International Workshop on Geographic Information Systems, pp. 241–249. ACM, New York (2004)
Yoo, J., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18, 1323–1337 (2006)
Wang, L., Bao, Y., Lu, J., Yip, J.: A new join-less approach for co-location pattern mining. In: 8th IEEE International Conference on Computer and Information Technology, pp. 197–202. Sydney (2008)
Wang, L., Bao, Y., Lu, Z.: Efficient discovery of spatial colocation patterns using the iCPI-tree. The Open Inf. Syst. J. 3, 69–80 (2009)
Yoo, J., Boulware, D., Kimmey, D.: A parallel spatial co-location mining algorithm based on mapreduce. In: International Congress on Big Data, pp. 25–31 (2014)
Yoo, J., Boulware, D., Kimmey, D.: Parallel co-location mining with MapReduce and NoSQL systems. Knowl Inf. Syst. (2019)
Andrzejewski, W., Boinski, P.: Efficient spatial co-location pattern mining on multiple GPUs. Expert Syst. Appl. 93, 465–483 (2018)
Sainju, A., Aghajarian, D., Jiang, Z., Prasad, S.: Parallel grid-based colocation mining algorithms on GPUs for big spatial event data. IEEE Trans Big Data, pp. 1–1 (2018)
Andrzejewski, W., Boinski, P: Parallel approach to incremental co-location pattern mining. Information Sci. 496, 485–505 (2019)
Leibovici, D., Claramunt, C., Guyader, D., Brosset, D.: Local and global spatio-temporal entropy indices based on distance-ratios and co-occurrences distributions. Int. J. Geogr. Inf. Sci. 28, 1061–1084 (2014)
Huang, Y., Pei, J., Xiong, H.: Mining co-location patterns with rare events from spatial data sets. GeoInformatica 10, 239–260 (2006)
Wang, L., Wu, P., Chen, H.: Finding probabilistic prevalent colocations in spatially uncertain data sets. IEEE Trans. Knowl. Data Eng. 25, 790–804 (2013)
Ouyang, Z., Wang, L., Wu, P.: Spatial co-location pattern discovery from fuzzy objects. Int. J. Artif Intell. Tools 26, 1750003 (2016). https://doi.org/10.1142/S0218213017500038
Yao, X., Chen, L., Peng, L., Chi, T.: A co-location pattern-mining algorithm with a density-weighted distance thresholding consideration. Inf. Sci. 396, 144–161 (2017)
Yoo, J., Bow, M.: Mining top-k closed co-location patterns. In: International Conference on Spatial Data Mining and Geographical Knowledge Service, pp. 100–105. IEEE, Fuzhou (2011)
Wang, L., Zhou, L., Lu, J., Yip, J.: An order-clique-based approach for mining maximal co-locations. Inf. Sci. 179, 3370–3382 (2009)
Wang, L., Bao, X., Zhou, L.: Redundancy reduction for prevalent co-location patterns. IEEE Trans. Knowl. Data Eng. 30, 142–155 (2018)
Wang, L., Bao, X., Chen, H., Cao, L.: Effective lossless condensed representation and discovery of spatial co-location patterns. Inf. Sci. 436–437, 197–213 (2018)
Boinski, P., Zakrzewicz, M.: Collocation pattern mining in a limited memory environment using materialized iCPI-tree. In: Data Warehousing and Knowledge Discovery, pp. 279–290. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-32584-7_23
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Tran, V., Pham, C., Do, T., Pham, H. (2023). Discovering Prevalent Co-location Patterns Without Collecting Co-location Instances. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_33
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DOI: https://doi.org/10.1007/978-981-99-5834-4_33
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