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Neighborhood Rough-Sets-Based Spatial Data Analytics
, B. K. Tripathy
Published in IGI Global
Pages: 415 - 426
Rough set theory partitions a universe using single-layered granulation. The equivalence classes induced by rough sets are based on discretized values. Considering the fact that the spatial data are continuous at large, discretizing them may cause loss of data. Neighborhood approximations can lead to closely related coverings using continuous values. Besides, the spatial attributes also need to be given due consideration and should be handled unlike non-spatial attributes in the process of dimensionality reduction. This chapter analyzes the use of neighborhood rough sets for continuous data and handling spatially correlated attributes using rough sets.
About the journal
JournalAdvances in Computer and Electrical Engineering Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics
PublisherIGI Global
Open Access0