Abstract
Co-location pattern mining aims at finding the subsets of spatial features whose instances are frequently located together in geographic space. Most studies mainly focus on whether spatial feature instances are frequently located together. However, the utilities of spatial instances in different space regions are different. Based on the importance of spatial a region, the utility value of the region is determined, and then a utility participation index of co-location patterns as a new interestingness measure is defined. We present a basic high utility co-location pattern mining algorithm. To reduce the computational cost, an improved mining algorithm with pruning strategy is developed by cutting down the search space. The experiments on synthetic and real world datasets show that the proposed methods are effective and efficient.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61472346, 61662086, 61762090), the Natural Science Foundation of Yunnan Province (2015FB114,2016FA026), the Project of Innovative Research Team of Yunnan Province (XT412011), and the Spectrum Sensing and Borderlands Security Key Laboratory of Universities in Yunnan (C6165903).
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Zhao, J., Wang, L., Yang, P., Chen, H. (2018). Mining High Utility Co-location Patterns Based on Importance of Spatial Region. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 849. Springer, Singapore. https://doi.org/10.1007/978-981-13-0896-3_5
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DOI: https://doi.org/10.1007/978-981-13-0896-3_5
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