A Fuzzy Rough Set Approach for Incrementally Updating Approximations in Hybrid Information Systems

In real-applications, there may exist missing data and many kinds of data e.g., categorical, real-valued and set-valued data in an information system which is called as a Hybrid Information System HIS. A new Hybrid Distance HD between two objects in HIS is developed based on the value difference metric. Then, a novel fuzzy rough set is constructed by using the HD distance and the Gaussian kernel. In addition, the information systems often vary with time. How to use the previous knowledge to update approximations in fuzzy rough sets is a key step for its applications on hybrid data. The fuzzy information granulation methods based on the HD distance are proposed. Furthermore, the principles of updating approximations in HIS under the variation of the attribute set are discussed. A fuzzy rough set approach for incrementally updating approximations is then presented. Some examples are employed to illustrate the proposed methods.

[1]  Yi Cheng,et al.  The incremental method for fast computing the rough fuzzy approximations , 2011, Data Knowl. Eng..

[2]  M. Narasimha Murty,et al.  Tree structure for efficient data mining using rough sets , 2003, Pattern Recognit. Lett..

[3]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[4]  Wang Guo-ying.  An incremental rule acquisition algorithm based on variable precision rough set model , 2005 .

[5]  Nehad N. Morsi,et al.  Axiomatics for fuzzy rough sets , 1998, Fuzzy Sets Syst..

[6]  Yi Pan,et al.  Parallel rough set based knowledge acquisition using MapReduce from big data , 2012, BigMine '12.

[7]  Tsau Young Lin,et al.  Rough Sets and Data Mining: Analysis of Imprecise Data , 1996 .

[8]  Hiroshi Tanaka,et al.  Incremental learning of probabilistic rules from clinical databases based on rough set theory , 1997, AMIA.

[9]  Sen Guo,et al.  A novel dynamic incremental rules extraction algorithm based on rough set theory , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[10]  Da Ruan,et al.  Incremental learning optimization on knowledge discovery in dynamic business intelligent systems , 2011, J. Glob. Optim..

[11]  Yiyu Yao.  Combination of Rough and Fuzzy Sets Based on α-Level Sets , 1997 .

[12]  Guoyin Wang,et al.  RRIA: A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm , 2003, Fundam. Informaticae.

[13]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[14]  Geert Wets,et al.  A rough sets based characteristic relation approach for dynamic attribute generalization in data mining , 2007, Knowl. Based Syst..

[15]  Shusaku Tsumoto,et al.  Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model , 2004, Inf. Sci..

[16]  Witold Pedrycz,et al.  Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications , 2010, Int. J. Approx. Reason..

[17]  Francisco Herrera,et al.  Combining Numerical and Linguistic Information in Group Decision Making , 1998, Inf. Sci..

[18]  Xizhao Wang,et al.  On the generalization of fuzzy rough sets , 2005, IEEE Transactions on Fuzzy Systems.

[19]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[20]  Andrzej Skowron,et al.  A rough set approach to knowledge discovery , 2002, International Journal of Intelligent Systems.

[21]  Pan Yunhe,et al.  A Parallel Approximate Rule Extracting Algorithm Based on the Improved Discernibility Matrix , 2004 .

[22]  Chengxiang Hu,et al.  An incremental updating principle for computing approximations in information systems while the object set varies with time , 2009, 2009 IEEE International Conference on Granular Computing.

[23]  Yang Xu,et al.  A GENERALIZED ROUGH SET APPROACH TO ATTRIBUTE GENERALIZATION IN DATA MINING , 2000 .

[24]  Da Ruan,et al.  Neighborhood rough sets for dynamic data mining , 2012, Int. J. Intell. Syst..

[25]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[26]  Chien-Chung Chan,et al.  A Rough Set Approach to Attribute Generalization in Data Mining , 1998, Inf. Sci..

[27]  Andrzej Skowron,et al.  EXTRACTING LAWS FROM DECISION TABLES: A ROUGH SET APPROACH , 1995, Comput. Intell..

[28]  Wen-Xiu Zhang,et al.  An axiomatic characterization of a fuzzy generalization of rough sets , 2004, Inf. Sci..

[29]  Yanyong Guan,et al.  Set-valued information systems , 2006, Inf. Sci..

[30]  Da Ruan,et al.  A parallel method for computing rough set approximations , 2012, Inf. Sci..