Zonal co-location pattern discovery with dynamic parameters

Mete Celik, James M. Kang, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Scopus citations

Abstract

Zonal co-location patterns represent subsets of feature-types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. Also, the set of candidate patterns is exponential in the number of feature types, and spatial datasets are huge. Previous studies have focused on discovering global spatial co-location patterns with a fixed interest measure threshold. In this paper, we propose an indexing structure for co-location patterns and propose algorithms (Zoloc-Miner) to discover zonal co-location patterns efficiently for dynamic parameters. Extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform näive alternatives.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Pages433-438
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other7th IEEE International Conference on Data Mining, ICDM 2007
CountryUnited States
CityOmaha, NE
Period10/28/0710/31/07

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    Celik, M., Kang, J. M., & Shekhar, S. (2007). Zonal co-location pattern discovery with dynamic parameters. In Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007 (pp. 433-438). [4470269] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2007.102