Author
Sharmila Banu K
Bio: Sharmila Banu K is an academic researcher from VIT University. The author has contributed to research in topics: Rough set & Spatial analysis. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.
Papers
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01 Jan 2019
TL;DR: This chapter analyzes the use of neighborhood rough sets for continuous data and handling spatially correlated attributes using rough sets in rough set theory.
Abstract: 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.
2 citations
01 Feb 2019
TL;DR: The major objective behind this paper is to measure the similarity of GIS subzones on a discrete dataset that has a varied number of applications not only in GIS and epidemiology but also in clusters analysis and artificial intelligence.
Abstract: There are many methods that can be used to compute the geographic similarity between regions represented through areas in a two dimensional space. However, it is the rough set based membership function that allows an estimate of the similarity between a subzone formed by the data. The major objective behind this paper is to measure the similarity of GIS subzones on a discrete dataset. This has a varied number of applications not only in GIS and epidemiology but also in clusters analysis and artificial intelligence.
Cited by
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TL;DR: Experimental results show that the proposed extension significantly increased classification or segmentation accuracy, and the spatial reduct required much less time than classical reduct.
Abstract: When classical rough set (CRS) theory is used to analyze spatial data, there is an underlying assumption that objects in the universe are completely randomly distributed over space. However, this a...
4 citations
01 Mar 2017
TL;DR: The MMeR algorithm with neighbourhood relations is generalized and made a neighbourhood rough set model which it is called MMeNR (Min Mean Neighborhood Roughness), which takes care of the heterogeneous data and also the uncertainty associated with it.
Abstract: In recent times enumerable number of clustering algorithms have been developed whose main function is to make sets of objects having almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and so have stability issues. Thus handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 MMR [1] algorithm was developed which was based on basic rough set theory. MMeR [2] was proposed in 2009 which surpassed the results of MMR in taking care of categorical data. It has the capability of handling heterogeneous data but only to a limited extent because it is based on classical rough set model. In this paper, we generalize the MMeR algorithm with neighbourhood relations and make it a neighbourhood rough set model which we call MMeNR (Min Mean Neighborhood Roughness). It takes care of the heterogeneous data and also the uncertainty associated with it. Standard data sets have been used to gauge its effectiveness over the other methods.