A Map-Reduce Framework for Finding Clusters of Colocation Patterns - A Summary of Results

Given an application of a spatial data set, we discover a set of co-location patterns using a GUI (Graphical User Interface) model in a less amount of time, as this application is implemented using a parallel approach-A Map-Reduce framework. This framework uses a grid based approach to find the neig...

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Bibliographic Details
Main Authors: Sheshikala, M., Rao, D. Rajeswara, Prakash, R. Vijaya
Format: Conference Proceeding
Language:English
Subjects:
Co-location rules
Conditional Probability
Conferences
Data mining
Data models
Euclidean distance
Graphical user interfaces
Map-Reduce Framework
Neighboring Paths
Security
Spatial data mining
Spatial databases
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Description
Summary:Given an application of a spatial data set, we discover a set of co-location patterns using a GUI (Graphical User Interface) model in a less amount of time, as this application is implemented using a parallel approach-A Map-Reduce framework. This framework uses a grid based approach to find the neighboring paths using a Euclidean distance. The framework also uses a dynamic algorithm in finding the spatial objects and discovers co-location rules from them. Once co-location rules are identified, we give the input as a threshold value which is used to form clusters of similar behavior. If the threshold value is too low more clusters are formed, if it is too high less clusters are formed. The comparison of the results shows that the proposed system is computationally good and gives the co-location patterns in a less amount of time.
ISSN:2473-3571
DOI:10.1109/IACC.2017.0039