Journal
REMOTE SENSING
Volume 14, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/rs14092103
Keywords
spatial data mining; negative co-location; join-based algorithm; directional mining
Categories
Funding
- National Natural Science of China [41961065]
- Guangxi Science and Technology Base and Talent Project [GuikeAD19254002, GuikeAA18242048, GuikeAA18118038]
- Guangxi Natural Science for Innovation Research Team [2019GXNSFGA245001]
- Guilin Research and Development Plan Program [20190210-2]
- National Key Research and Development Program of China [2016YFB0502501]
- BaGuiScholars program of Guangxi
- Innovation Project of Guangxi Graduate Education [YCBZ2021061]
- Guangxi Key Laboratory of Spatial Information and Geomatics Program [19-050-11-14]
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This paper proposes a join-based prevalent negative co-location mining algorithm, which can quickly and effectively mine all the prevalent negative co-location patterns in spatial data. The experiment results demonstrate that the algorithm has an excellent efficiency level.
It is usually difficult for prevalent negative co-location patterns to be mined and calculated. This paper proposes a join-based prevalent negative co-location mining algorithm, which can quickly and effectively mine all the prevalent negative co-location patterns in spatial data. Firstly, this paper verifies the monotonic nondecreasing property of the negative co-location participation index (PI) value as the size increases. Secondly, using this property, it is deduced that any prevalent negative co-location pattern with size n can be generated by connecting prevalent co-location with size 2 and with an n - 1 size candidate negative co-location pattern or an n - 1 size prevalent positive co-location pattern. Finally, the experiment results demonstrate that while other conditions are fixed, the proposed algorithm has an excellent efficiency level. The algorithm can eliminate the 90% useless negative co-location pattern maximumly and eliminate the useless 40% negative co-location pattern averagely.
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