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Research of Attribute Reduction Algorithm Based on Rough Set

Author: LiangMeng
Tutor: JiangBaoQing
School: Henan University
Course: Applied Computer Technology
Keywords: Decision table Attribute Reduction Difference matrix Difference function Next set of maximal elements
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 158
Quote: 4
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Abstract


Artificial intelligence, knowledge representation is an important issue . From the point of view of rough sets , knowledge is a classification ability of things , so the use of two-dimensional table to express knowledge , and based on a subset of attributes in the table objects into different categories to go. Rough set is divided to distinguish between the use and processing of incomplete , inaccurate , inconsistent , and other uncertain information as a tool . Attribute reduction algorithm is one of the core rough sets , rough sets in intelligent information processing is an important method, but also an important research topic of knowledge discovery . How to get effective and fast algorithm for attribute reduction , knowledge reduction algorithm is important , but also the rough set method can be effectively applied protection. Firstly, from the concept and nature of rough set to start , followed by reduction of knowledge studied in several important Reduction Algorithm ; and thus extends to the right decision table attribute reduction algorithm. In the decision table attribute reduction algorithm , Skowron difference matrix method plays an important role , because of its relatively easy to implement , and many classical algorithms are built on the basis of this approach . This paper studies the full attribute reduction algorithms, including the following aspects : 1. Studied the decision table attribute reduction algorithm , which includes the optimal minimum relative reduction algorithms and fully attribute reduction algorithm . Two classes of algorithms and further merits and efficiency. (2) further studied based on differences in the function attribute reduction algorithm , the algorithm found problems , and the idea of ??using the Cartesian product effectively improve the efficiency of the algorithm . 3 studied based on Skowron difference matrix relative core and attribute importance based on Pawlak seeking relative core two methods , experimental analysis of the use of nuclear Skowron difference matrix relative advantage in efficiency . 4 studied the relative reduction with the next set of relationships between maximal elements ; same time with different matrix method Skowron obtained relative core , and on this basis, the use Boundary algorithm to the next set of ideas into successful attribute reduction in propose a new attribute reduction algorithm .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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