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Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Neighborhood Rough-Sets-Based Spatial Data Analytics

Neighborhood Rough-Sets-Based Spatial Data Analytics
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Author(s): Sharmila Banu K. (VIT University, India) and B. K. Tripathy (VIT University, India)
Copyright: 2018
Pages: 10
Source title: Encyclopedia of Information Science and Technology, Fourth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-2255-3.ch160


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Rough Set Theory partitions a universe using single layered granulation. The equivalence classes induced by rough sets are based on discretised values. Considering the fact that the spatial data are continuous at large, discretising 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 analyses the use of Neighborhood rough sets for continuous data and handling spatially correlated attributes using rough sets.

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