Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2505-2509, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2505-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2505-2509, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2505-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  02 May 2018

02 May 2018

MINING CO-LOCATION PATTERNS WITH CLUSTERING ITEMS FROM SPATIAL DATA SETS

G. Zhou1, Q. Li1,2, G. Deng2, T. Yue1, and X. Zhou1 G. Zhou et al.
  • 1Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian’gan Road, Guilin, Guangxi 541004, China
  • 2College of Science, Guilin University of Technology, No. 12 Jian’gan Road, Guilin, Guangxi 541004, China

Keywords: Spatial data mining, Clustering items, Co-location patterns with clustering items, Neighbor relationship, Praticipation index

Abstract. The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.