Premium
Neighborhood rough sets for dynamic data mining
Author(s) -
Zhang Junbo,
Li Tianrui,
Ruan Da,
Liu Dun
Publication year - 2012
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21523
Subject(s) - rough set , categorical variable , computer science , data mining , set (abstract data type) , data set , dynamic data , algorithm , artificial intelligence , machine learning , database , programming language
Approximations of a concept in rough set theory induce rules and need to update for dynamic data mining and related tasks. Most existing incremental methods based on the classical rough set model can only be used to deal with the categorical data. This paper presents a new dynamic method for incrementally updating approximations of a concept under neighborhood rough sets to deal with numerical data. A comparison of the proposed incremental method with a nonincremental method of dynamic maintenance of rough set approximations is conducted by an extensive experimental evaluation on different data sets from UCI. Experimental results show that the proposed method effectively updates approximations of a concept in practice. © 2012 Wiley Periodicals, Inc.