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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 69-76
Junli Lu , Department of Computer Science and Engineering, Yunnan University, Kunming, China
Lizhen Wang , Department of Computer Science and Engineering, Yunnan University, Kunming, China
Yuan Fang , Department of Computer Science and Engineering, Yunnan University, Kunming, China
Xuguang Bao , Department of Computer Science and Engineering, Yunnan University, Kunming, China
ABSTRACT
Spatial co-locations represent the subsets of spatial features which are frequently located together in a geographic space. Discovering co-locations has many useful applications. For example, co-located plant species discovered from plant distribution datasets can contribute to the analysis of plant geography, phytosociology studies, and plant protection recommendations. This paper focuses on incremental mining of co-locations. Because of the speed of updated data and the difficulty of incremental mining of co-locations, incremental mining of co-locations should be given more attention. In this paper, a novel method of incremental mining is proposed, which begins with maximal prevalent co-locations in old database, to search the dividing line that partitions the prevalent and non-prevalent co-locations in updated database. For a co-location candidate, the new measure, updated participation ratio (index), is used to evaluate its prevalence in the updated database. This can be easily done by using the old co-location instances, querying the disappeared co-location instances, and generating the added co-location instances. Next, a pruning strategy can prune part of co-locations for improving the efficiency. At last, the experiments evaluate the efficiency of proposed methods.
INDEX TERMS
Data mining, Spatial databases, Algorithm design and analysis, Indexes, Atmospheric measurements, Particle measurements
CITATION

Junli Lu, Lizhen Wang, Yuan Fang and Xuguang Bao, "A novel method on incremental mining of spatial co-locations," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 69-76.
doi:10.1109/BIGCOMP.2016.7425803
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