2012
DOI: 10.5120/5836-7994
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Mining Of Spatial Co-location Pattern from Spatial Datasets

Abstract: Spatial data mining, or knowledge discovery in spatial database, refers to the extraction of implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases. Spatial data mining is the process of discovering interesting characteristics and patterns that may implicitly exist in spatial database. A huge amount of spatial data and newly emerging concept of Spatial Data Mining which includes the spatial distance made it an arduous task. Knowledge discovery in spatial databases i… Show more

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Cited by 6 publications
(5 citation statements)
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“…Discovering spatial co-location patterns from respective databases is the primary job of spatial data mining in numerous applications (Kumar et al, 2012c) and such co-Science Publications AJAS location patterns depict the subsets of spatial features whose objects are typically located in close geographic proximity. For example, the co-location patterns are drawn in the areas like symbiotic species in ecology such as the Nile crocodile and Egyptian plover, frontage roads and highways in metropolitan road maps and co-located services often requested and located together from mobile devices (e.g., PDAs and cellular phones) in location-based services (Huang and Zhang, 2006;Kim et al, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…Discovering spatial co-location patterns from respective databases is the primary job of spatial data mining in numerous applications (Kumar et al, 2012c) and such co-Science Publications AJAS location patterns depict the subsets of spatial features whose objects are typically located in close geographic proximity. For example, the co-location patterns are drawn in the areas like symbiotic species in ecology such as the Nile crocodile and Egyptian plover, frontage roads and highways in metropolitan road maps and co-located services often requested and located together from mobile devices (e.g., PDAs and cellular phones) in location-based services (Huang and Zhang, 2006;Kim et al, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…The participation ratio of a spatial feature can be defined as a measure to find how a spatial feature f is co-located with other spatial features in the co-location pattern L, given a spatial database S [5]. The participation ratio Pr for a feature type fi of a co-location L if the fraction of instances of fi which participate in any row instance of co-location L. The participation ratio pr can be expressed in the form - …”
Section: Participation Ratiomentioning
confidence: 99%
“…A Co-location rule is of the form: L1 → L2 (p, cp) where L1 and L2 are co-locations, p is prevalence measure and cp is the conditional probability [5]. For example, "Nile Crocodiles → Egyptian Plover" predicts the presence of Egyptian Plover birds in areas with Nile Crocodiles.…”
Section: Co-location Rules Modelingmentioning
confidence: 99%
“…The GISs provide the user with the possibility of querying a territory for extracting areas that exhibit certain properties, i.e., given combinations of values of the attributes. This explosively growing spatial data creates the necessity of knowledge/information discovery from spatial data, which leads to a promising emerging field, called spatial data mining or knowledge discovery in spatial databases [3]. Regionalization has been an important and challenging problem for a large spectrum of research and application domains, for example, climatic zoning [5], eco region analysis [6], hazards and disasters management [7], map generalization [8], location optimization [9], census reengineering [10], and health-related analysis [11].…”
Section: Introductionmentioning
confidence: 99%