Abstract
Spatial co-location rule mining plays an important role in spatial data mining. The usefulness of co-location rules is strongly limited by the huge amount of discovered rules. To overcome this drawback, several methods were proposed in the literatures. However, being generally based on statistical information, most of these methods do not guarantee that the extracted co-location rules are interesting for the user. Thus, it is crucial to help the decision-maker with an efficient processing step to reduce the number of co-location rules. This demonstration presents OICRM, an interactive system to discover interesting co-location rules based on the ontology. First, the ontology is used to improve the integration of user knowledge; next, a powerful formula sub-system is designed to easily represent domain’s background and constraint knowledge; finally, OICRM has an interactive post-processing step (the secondary mining) to reduce the number of rules furthermore.
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Acknowledgements
This work was supported in part by grants (No. 61472346, No. 61262069) from the National Natural Science Foundation of China and in part by a grant (No. 2016FA026, No. 2015FB149, and No. 2015FB114) from the Science Foundation of Yunnan Province.
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Bao, X., Wang, L., Wang, M. (2016). OICRM: An Ontology-Based Interesting Co-location Rule Miner. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_67
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DOI: https://doi.org/10.1007/978-3-319-45817-5_67
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