An incremental algorithm for mining classification rules in incomplete information systems

One of important research areas in data mining is to develop methods to update knowledge by using the existing knowledge, since it can generally enhance mining efficiency, especially for very large database. Rough set, a new mathematical tool to deal with vagueness and uncertainty, has been successfully applied in data mining. Presently, several approaches based on rough set have been proposed for mining tasks when knowledge updates. However, it only aims at complete information systems. In fact, many information systems are incomplete in practical application. Therefore, in order to support more effective data mining tasks, it is meaningful to develop approaches to update knowledge in incomplete information systems (US). A method for incremental updating approximations of a concept was proposed by T. R. Li, et al (2003), which may realize adding and deleting a few attributes simultaneously at a time. Based on this method, we develop an incremental algorithm for mining classification rules from IIS. Complexity analysis of the algorithm and example show that our method can realize updating knowledge effectively.

[1]  Tzung-Pei Hong,et al.  Learning rules from incomplete training examples by rough sets , 2002, Expert Syst. Appl..

[2]  Philip S. Yu,et al.  Data Mining: How Research Meets Practical Development? , 2003, Knowledge and Information Systems.

[3]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[4]  David A. Bell,et al.  Computational methods for rough classification and discovery , 1998, KDD 1998.

[5]  Zdzislaw Pawlak,et al.  Drawing conclusions from data - The rough set way , 2001, Int. J. Intell. Syst..

[6]  Z. Pawlak.  Drawing conclusions from data—The rough set way , 2001 .

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

[8]  Marzena Kryszkiewicz,et al.  Rough Set Approach to Incomplete Information Systems , 1998, Inf. Sci..

[9]  Marzena Kryszkiewicz.  Comparative study of alternative types of knowledge reduction in inconsistent systems , 2001, Int. J. Intell. Syst..

[10]  Chang Li-yun,et al.  An Approach for Attribute Reduction and Rule Generation Based on Rough Set Theory , 1999 .

[11]  J. Kacprzyk,et al.  Incomplete Information: Rough Set Analysis , 1997 .

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

[13]  Andrzej Skowron,et al.  Synthesis of Decision Systems from Data Tables , 1997 .

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

[15]  Akira Nakamura,et al.  A rough logic based on incomplete information and its application , 1996, Int. J. Approx. Reason..

[16]  Daniel Vanderpooten,et al.  A Generalized Definition of Rough Approximations Based on Similarity , 2000, IEEE Trans. Knowl. Data Eng..

[17]  David A. Bell,et al.  Computational Methods for Rough Classification and Discovery , 1998, J. Am. Soc. Inf. Sci..

[18]  Yiyu Yao,et al.  Data mining using extensions of the rough set model , 1998, KDD 1998.

[19]  Yang Xu,et al.  An approach to attribute generalization in incomplete information systems , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[20]  Wojciech Ziarko,et al.  A METHOD FOR COMPUTING ALL MAXIMALLY GENERAL RULES IN ATTRIBUTE‐VALUE SYSTEMS , 1996, Comput. Intell..

[21]  Shusaku Tsumoto.  Rule Discovery in Databases with Missing Values Based on Rough Set Model , 1999, PAKDD.

[22]  Roman Slowinski,et al.  Rough-Set Reasoning about Uncertain Data , 1996, Fundam. Informaticae.

[23]  Marzena Kryszkiewicz,et al.  Rules in Incomplete Information Systems , 1999, Inf. Sci..

[24]  Tian Li,et al.  An improving mining algorithm aiming at a kind of specific function of degree of interest , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[25]  Jan Komorowski,et al.  Rough Sets for Data Mining and Knowledge Discovery (Abstract) , 1997, PKDD.