GDL

Event Centric Modeling Approach in Colocation Pattern Analysis From Spatial Data

ISSN : 0975-5705
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : Venkatesan.M, Arunkumar.Thangavelu and Prabhavathy.P, International Journal of Database Management Systems
Dibuat : 2012-06-26, dengan 1 file

Keyword : Co-location Pattern, Association Rule, Spatial Data, Participation Index
Subjek : Event Centric Modeling Approach in Colocation Pattern Analysis From Spatial Data
Url : http://airccse.org/journal/ijdms/papers/3311ijdms11.pdf
Sumber pengambilan dokumen : Internet

Spatial co-location patterns are the subsets of Boolean spatial features whose instances are often located in


close geographic proximity. Co-location rules can be identified by spatial statistics or data mining


approaches. In data mining method, Association rule-based approaches can be used which are further


divided into transaction-based approaches and distance-based approaches. Transaction-based approaches


focus on defining transactions over space so that an Apriori algorithm can be used. The natural notion of


transactions is absent in spatial data sets which are embedded in continuous geographic space. A new


distance –based approach is developed to mine co-location patterns from spatial data by using the concept


of proximity neighborhood. A new interest measure, a participation index, is used for spatial co-location


patterns as it possesses an anti-monotone property. An algorithm to discover co-location patterns are


designed which generates candidate locations and their table instances. Finally the co-location rules are


generated to identify the patterns.

Deskripsi Alternatif :

Spatial co-location patterns are the subsets of Boolean spatial features whose instances are often located in


close geographic proximity. Co-location rules can be identified by spatial statistics or data mining


approaches. In data mining method, Association rule-based approaches can be used which are further


divided into transaction-based approaches and distance-based approaches. Transaction-based approaches


focus on defining transactions over space so that an Apriori algorithm can be used. The natural notion of


transactions is absent in spatial data sets which are embedded in continuous geographic space. A new


distance –based approach is developed to mine co-location patterns from spatial data by using the concept


of proximity neighborhood. A new interest measure, a participation index, is used for spatial co-location


patterns as it possesses an anti-monotone property. An algorithm to discover co-location patterns are


designed which generates candidate locations and their table instances. Finally the co-location rules are


generated to identify the patterns.

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