论文信息 - Rules Extraction from Multiple Decisions Ordered Information Tables

Rules Extraction from Multiple Decisions Ordered Information Tables

Ordered information table is one of the most important research areas of granular computing. In this thesis, we introduce multiple decisions ordered information tables based on the concept of ordered information tables. Multiple decisions ordered information tables are used to describe the actual multiple decision attributes situation of reality. We study the process of rule extraction from multiple decisions ordered information tables thoroughly and several concepts about this process are proposed and discussed. At last, an example of multiple decisions ordered information tables is used to illustrate the basic ideas. These ideas and methods are quite useful for KDD, DM and GC.

[1] S. Tsumoto,et al. Rough Set Theory and Granular Computing , 2003 .

[2] Yiyu Yao,et al. An Analysis of Quantitative Measures Associated with Rules , 1999, PAKDD.

[3] Janusz Zalewski,et al. Rough sets: Theoretical aspects of reasoning about data , 1996 .

[4] Yiyu Yao,et al. Mining Ordering Rules Using Rough Set Theory , 2008 .

[5] Aleksander Ohrn,et al. ROSETTA -- A Rough Set Toolkit for Analysis of Data , 1997 .

[6] Yiyu Yao,et al. Measuring Retrieval Effectiveness Based on User Preference of Documents , 1995, J. Am. Soc. Inf. Sci..

[7] Yiyu Yao,et al. Information tables with neighborhood semantics , 2000, SPIE Defense + Commercial Sensing.

[8] Yiyu Yao,et al. Data analysis and mining in ordered information tables , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[9] Lotfi A. Zadeh,et al. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[10] Salvatore Greco,et al. Rough approximation of a preference relation by dominance relations , 1999, Eur. J. Oper. Res..

[11] Yiyu Yao,et al. Mining High Order Decision Rules , 2003 .