Extended Random Sets for Knowledge Discovery in Information Systems

In this paper, we discuss the problem of knowledge discovery in information systems. A model is presented for users to obtain either "objective" interesting rules or "subjective" judgments of meaningful descriptions based on their needs. Extended random sets are presented firstly to describe the relationships between condition granules and decision granules. The interpretation is then given to show what we can obtain from the extended random sets.

[1]  Yiyu Yao,et al.  Induction of Classification Rules by Granular Computing , 2002, Rough Sets and Current Trends in Computing.

[2]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[3]  Liu Dayo.  THE INTERPRETATION OF GENERALIZED EVIDENCE THEORY , 1997 .

[4]  T. Y. Lin,et al.  Rough Sets and Data Mining , 1997, Springer US.

[5]  Roman Słowiński,et al.  Generalized Decision Algorithms, Rough Inference Rules, and Flow Graphs , 2002, Rough Sets and Current Trends in Computing.

[6]  Rudolf Kruse,et al.  Uncertainty and Vagueness in Knowledge Based Systems , 1991, Artificial Intelligence.

[7]  Yiyu Yao,et al.  On modeling data mining with granular computing , 2001, 25th Annual International Computer Software and Applications Conference. COMPSAC 2001.

[8]  Tsau Young Lin,et al.  A Review of Rough Set Models , 1997 .

[9]  Rudolf Kruse,et al.  Uncertainty and vagueness in knowledge based systems: numerical methods , 1991, Artificial intelligence.

[10]  P. Walley.  Measures of Uncertainty in Expert Systems , 1996, Artificial Intelligence.

[11]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[12]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[13]  Zdzislaw Pawlak,et al.  In Pursuit of Patterns in Data Reasoning from Data - The Rough Set Way , 2002, Rough Sets and Current Trends in Computing.