MMeNR: Neighborhood rough set theory based algorithm for clustering heterogeneous data - Tripathy, B.K.; Goyal, Akarsh; Chowdhury, Rahul; Banu K., Sharmila (2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 2017)
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.