Abstract
Co-location pattern mining is an important task in spatial data mining. However, the availability of the discovered co-location patterns is limited due to lack of specific target. Unlike existing works, we consider the dominant relation as a specific target in co-location pattern mining process. This demonstration presents DFCPM (Dominant Feature Co-location Pattern Miner), a system for users who not only take an interest in the prevalence of a feature set, but also concern which features play the dominant role in a pattern. Given a set of POIs (Point of Interest) data, we evaluate and identify the co-location patterns which are prevalent and contain dominant features. Also, DFCPM extracts the dominant features from each DFCP (Dominant Feature Co-location Pattern) to provide more information and help the decision making.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (61472346, 61662086, 61762090), the Natural Science Foundation of Yunnan Province (2015FB114, 2016FA026), and the Project of Innovative Research Team of Yunnan Province and the Project of Yunnan University Graduate Student Scientific Research (YDY17110).
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Fang, Y., Wang, L., Hu, T., Wang, X. (2018). DFCPM: A Dominant Feature Co-location Pattern Miner. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_38
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DOI: https://doi.org/10.1007/978-3-319-96890-2_38
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