Rough Sets Theory as Symbolic Data Mining Method: An Application on Complete Decision Table

2013 ◽  
Vol 2 (1) ◽  
pp. 35-47 ◽  
Author(s):  
Mert Bal
Keyword(s):  
Data Mining ◽  
Rough Sets ◽  
Decision Table ◽  
Mining Method ◽  
Rough Sets Theory ◽  
Data Mining Method ◽  
Symbolic Data ◽  
Sets Theory

Feature Extraction from Engine's Data Based on Rough Sets Theory

2011 ◽  
Vol 120 ◽  
pp. 410-413
Author(s):  
Feng Wang ◽  
Li Xin Jia
Keyword(s):  
Feature Extraction ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Technical Condition ◽  
Decision Table ◽  
Discernibility Matrix ◽  
Rough Sets Theory ◽  
Sets Theory

The speed signal of engine contains abundant information. This paper introduces rough set theory for feature extraction from engine's speed signals, and proposes a method of mining useful information from a mass of data. The result shows that the discernibility matrix algorithm can be used to reduce attributes in decision table and eliminate unnecessary attributes, efficiently extracted the features for evaluating the technical condition of engine.

One Fusion Approach of Fault Diagnosis Based on Rough Sets Theory and Dempster-Shafer Theory

2012 ◽  
Vol 241-244 ◽  
pp. 405-409 ◽  
Author(s):  
Yan Qin Su ◽  
Ji Hong Cheng ◽  
Ting Xue Xu
Keyword(s):  
Fault Diagnosis ◽  
Rough Sets ◽  
Decision Table ◽  
Combination Rules ◽  
Rough Sets Theory ◽  
Dempster Shafer Theory ◽  
Basic Probability ◽  
Shafer Theory ◽  
Sets Theory ◽  
Fusion Approach

There is the advantage of Rough Sets Theory for redundant condition reduction and D-S Theory for combination rules reasoning, one fusion approach based on the two theories was given. Firstly, the test data was discretizated and attribution reduced to get the reduction decision table. Then, the basic probability assignment was gotten through calculating the condition attribution of the decision table while the condition attribution was regarded as evidence input and the decision attribution as discernment frame. Finally, the evidence was combination reasoned and the fault diagnosis results were gotten, and the application example was verified its validity.

A Novel Data Mining Method on Quality Control within Spinning Process

2012 ◽  
Vol 224 ◽  
pp. 87-92 ◽  
Author(s):  
Zhi Jun Lv ◽  
Qian Xiang ◽  
Jian Guo Yang
Keyword(s):  
Data Mining ◽  
Mining Method ◽  
Initial Strength ◽  
Rough Sets Theory ◽  
Yarn Strength ◽  
Data Mining Method ◽  
Complex Information ◽  
Key Factor ◽  
Spinning Process ◽  
Fiber Materials

Rough sets theory (RST) is a new data mining method that effectively deals with the problems with inexact, uncertain or vague knowledge in a complex information system. This paper investigates knowledge discovery methods from the textile industrial database, and then presents a RST-based intelligent control model (ICM) for spinning process. In order to analyze the yarn strength when the characteristics of fibers are given, a rule extraction method based on RST is researched. The logical rules extracted from the decision table indicate that the initial strength of fibers is a key factor influencing on the yarn strength. At the same time, the different values combination of the final reduced attributes also obviously influence on the yarn strength in different degree when the certain nominal yarn is being processed. Therefore, RST method can be taken into account for spinners to choose suitable fiber materials in order to ensure the quality and reduce cost.

The Rough Sets Theory and Evidence Theory

1990 ◽  
Vol 13 (3) ◽  
pp. 245-262
Author(s):  
Andrzej Skowron
Keyword(s):  
Rough Sets ◽  
Evidence Theory ◽  
Decision Table ◽  
Combination Rule ◽  
Approximation Space ◽  
Approximation Spaces ◽  
Rough Sets Theory ◽  
Decision Attributes ◽  
Lower And Upper Approximations ◽  
Sets Theory

The aim of the paper is to show some connections between the rough sets theory and the Dempser-Shafer approach. We prove that for every Pawlak’s approximation space there exists a Dempster-Shafer space with the qualities of the lower and upper approximations of sets in the approximation space equal to the credibility and plausibility of sets in the Dempster-Shafer space, respectively. Analogous connections hold between approximation spaces generated by the decision tables and Dempster-Shafer spaces, namely for every decision table space there exists a Dempster-Shafer space such that the qualities of the lower and upper approximations (with respect to the condition attributes) of sets definable in the decision table by condition and decision attributes coincide with the credibility and plausibility of sets in the Dempster-Shafer space, respectively. A combination rule in approximation spaces analogous to the combination rule used in the Dempster approach is derived.

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