NDLI: Discovery of personalized knowledge based on rough set theory

Content Provider	IEEE Xplore Digital Library
Author	Meng Zu-Qiang Cai Zi-Xing
Copyright Year	2003
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Central South Univ., Changsha, China (Meng Zu-Qiang; Cai Zi-Xing)
Abstract	In real world, a database usually is shared by many user, and all kind of knowledge can be take out of it. But it is not all the knowledge can meet a given user's need. Some of them is redundant. The redundant knowledge not only is of utility to the user, but also intervenes the given user in their work. So finding just the knowledge which a given user really needs is important work in data mining. Unfortunately, it is a shortcoming in most currently existing knowledge mining algorithms that the algorithms usually generate redundant knowledge, which cannot satisfy user-defined requirements. In this paper, a concept of personalized knowledge, which can just satisfy the requirements, is given. To finding personalized knowledge, rough set theory is introduced. First, with rough set theory, and based on discernibility matrice, an other discernibility matrice, personalized discernibility matrice, is designed. By using this kind of matrice, a reduct, which includes most attributes that the user interested in, called personalized reduct, written as p/spl I.bar/reduct, can be found. Secondly, by the way of designing threshold, redundant rules are delete. At last, we can describe a method for mining personalized knowledge, settling the existing problem to some extent.
Sponsorship	Chinese High-tech Development Program Chinese Academy of Sci. Chinese Soc. of Automation IEEE Systems, Man and Cybernetics Soc
Starting Page	1322
Ending Page	1327
File Size	500586
Page Count	6
File Format	PDF
ISBN	078037925X
DOI	10.1109/RISSP.2003.1285784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computer aided instruction Data analysis Databases Humans Set theory Educational institutions Biochemistry Data mining Medical diagnosis
Content Type	Text
Resource Type	Article

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