Rough set approach to incomplete numerical data

The theory rough set successfully implemented diberbagai sector , but rough set model classical can only associated with the data complete and set of data in symbolic form ( Jianhua , dai , 2013 ) . Research by adopting the theory rough set conducted in attribute numerical and the value of an attribute lost ( Jerzy w , Grzymala-Buse and Zdzislaw S , Hippe , Nov .2011 ) .In this research discussed how distinguish 2 ( two ) interprestasi of the value of an attribute lost and the condition of the value of an attribute who ignored ( lost values “do not care” conditions) with use the mining LERS in the data mining , with the approach probability and approach rough set , the results of the analysis that with the approach probability no better than approach rough set . By using testing wilcoxon the result of this research produce that there is no significant difference ( 5 % ). Many researchers have did research on the theory rough set to handles data containing attribute numerical and the data having been missing values simultaneously . In this research researchers proposed the theory of rough set that was expanded by see tolerance fuzzy to handle types of data on with see attribute the data and values lost or data that is not complete . At first the theory rough set used for numerical data that complete ( Jerzy w , Grzymala Buse , 2007 ) ( with all value the attribute of being specified ) . Lately the theory rough extended to set numerical data that is not complete ( with a value of an attribute that lost ) ( Jerzy w , Grzymala Buse , 2003 ) ( Jerzy w , Grzymala Buse , 2004 ) ( Jerzy W , Grzymala Buse , 2004 ) ( Jerzy w , Grzymala et al . , 2000 ) ( Jerzy W , Grzymala and A , Y , Wang , 1997 ) (M , Kryszkiewicz , Comment [u1]: jUDUL belum lengkap, masih menggantung. Judul untuk BUKU mungkin sesuai, tetapi ini research proposal.