Abstract
In spatial data mining, the usefulness of co-location patterns is strongly limited by the huge amount of delivered co-location patterns. Although many methods have been proposed to reduce the number of co-location patterns, most of them do not guarantee that the extracted co-location patterns are interesting for the user for being generally based on statistical information. This demonstration presents OICPM, an interactive system to discover interesting co-location patterns based on ontologies. With OICPM, the user can find his/her real interesting ones from a massive amount of co-location patterns efficiently within only a few rounds of selection, and the mined interesting co-location patterns are filtered in order for better decision.
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Acknowledgements
This work was supported in part by grants (No. 61472346, No. 61662086) from the National Natural Science Foundation of China, by grants (No. 2016FA026, No .2015FB114) from the Science Foundation of Yunnan Province and by the Spectrum Sensing and borderlands Security Key Laboratory of Universities in Yunnan (C6165903).
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Bao, X., Wang, L., Xiao, Q. (2017). OICPM: An Interactive System to Find Interesting Co-location Patterns Using Ontologies. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_29
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DOI: https://doi.org/10.1007/978-3-319-63564-4_29
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