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Parallel Approach for Finding Co-location Pattern – A Map Reduce Framework
- Source :
- Procedia Computer Science. :341-348
- Publisher :
- The Author(s). Published by Elsevier B.V.
-
Abstract
- Spatial co-location pattern mining is a sub field of data mining which is used to discover interesting patterns which are expressed as co-location rules. The objects that are frequently located in certain region are expressed as spatial co-locations. It presents a challenge for finding co-location patterns as the traditional data is considered discrete whereas the spatial objects are embedded in a continuous space. For this a join-less approach is proposed, but as the data size increases, a large amount of computation time is devoted to find co-location rules as the approach is purely sequential. We propose a parallelized join-less approach which finds the spatial neighbor relationship in order to identify co-location instances and co-location rules. The proposed work decreases the computation time drastically as it uses a Map-Reduce framework. This paper presents precise and completeness of the new approach. Finally, an experimental evaluations using synthetic data sets show the algorithm is computationally more efficient.
- Subjects :
- Co-location Mining
Computer science
Map Reduce
02 engineering and technology
Space (commercial competition)
computer.software_genre
Field (geography)
Participation Ratio
Location pattern
020204 information systems
Map reduce
Participation Index
0202 electrical engineering, electronic engineering, information engineering
General Earth and Planetary Sciences
Data Mining
020201 artificial intelligence & image processing
Data mining
Completeness (statistics)
computer
General Environmental Science
Subjects
Details
- Language :
- English
- ISSN :
- 18770509
- Database :
- OpenAIRE
- Journal :
- Procedia Computer Science
- Accession number :
- edsair.doi.dedup.....6f5e0a1ff0b9dcea8327a9934761e049
- Full Text :
- https://doi.org/10.1016/j.procs.2016.06.081