Application of rough set theory in accident analysis at work: A case study

2017 ◽  
Author(s):  
Sobhan Sarkar ◽  
Soumyadeep Baidya ◽  
J Maiti
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Accident Analysis

Fuzzy Miner

2011 ◽  
pp. 299-321 ◽  
Author(s):  
Nikos Pelekis ◽  
Babis Theodoulidis ◽  
Ioannis Kopanakis ◽  
Yannis Theodoridis
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Selection ◽  
Internet Routing ◽  
Data Mining Approach ◽  
New Information

QOSP Quality of Service Open Shortest Path First based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision-making. This paper focuses on routing algorithms and their appli-cations for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory offers a knowledge discovery approach to extracting routing-decisions from attribute set. The extracted rules can then be used to select significant routing-attributes and make routing-selections in routers. A case study is conducted to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algorithm based on data mining and rough set offers a promising approach to the attribute-selection prob-lem in internet routing.

The Application of Data Mining in the Civil Aviation Accident Analysis

2012 ◽  
Vol 241-244 ◽  
pp. 3000-3004
Author(s):  
Dai Wu Zhu ◽  
Yin Ni
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Passive State ◽  
Civil Aviation ◽  
Accident Analysis ◽  
Analysis Method ◽  
Accident Prediction

At present, our analysis of the aviation accident mainly limited to the methods of mathematical statistics, the analysis method means of a single, and in a passive state, so the accident prediction is poor. This paper, basis on the rough set theory in data mining and preferential information ,we improve the rough set attribute reduction algorithm, and applied to civil aviation accident analysis to indentify the potential law of accident.

Similarity measure for multi-granularity rough approximations of vague sets

2021 ◽  
Vol 40 (1) ◽  
pp. 1609-1621
Author(s):  
Jie Yang ◽  
Wei Zhou ◽  
Shuai Li
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Target Concept ◽  
Uncertainty Measure ◽  
Approximation Spaces ◽  
Vague Sets ◽  
Rough Approximation ◽  
Vague Concept

Vague sets are a further extension of fuzzy sets. In rough set theory, target concept can be characterized by different rough approximation spaces when it is a vague concept. The uncertainty measure of vague sets in rough approximation spaces is an important issue. If the uncertainty measure is not accurate enough, different rough approximation spaces of a vague concept may possess the same result, which makes it impossible to distinguish these approximation spaces for charactering a vague concept strictly. In this paper, this problem will be solved from the perspective of similarity. Firstly, based on the similarity between vague information granules(VIGs), we proposed an uncertainty measure with strong distinguishing ability called rough vague similarity (RVS). Furthermore, by studying the multi-granularity rough approximations of a vague concept, we reveal the change rules of RVS with the changing granularities and conclude that the RVS between any two rough approximation spaces can degenerate to granularity measure and information measure. Finally, a case study and related experiments are listed to verify that RVS possesses a better performance for reflecting differences among rough approximation spaces for describing a vague concept.

An approach to the analysis of design concepts using the rough set theory

2004 ◽  
Vol 18 (4) ◽  
pp. 343-355 ◽  
Author(s):  
D. ALISANTOSO ◽  
L.P. KHOO ◽  
I.B.H. LEE
Keyword(s):  
Information System ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Design Knowledge ◽  
Dynamic Design ◽  
Design Evolution ◽  
Design Concepts ◽  
Design Specifications

This paper describes an approach to the analysis of design concepts (DCs) using the rough set theory. The proposed approach attempts to extract design knowledge from past designs, and used the knowledge obtained to perform DC–capability mapping in a dynamic design evolution environment. The mapping enables designers to estimate the feasibility of a DC to meet stipulated design specifications. The proposed approach encompasses two algorithms, namely, the dissimilar objects algorithm and the attribute decomposition algorithm, to deal with an information system with unavailable information and multidecision attributes, respectively. The details of these algorithms are presented. A case study on the design of vacuum cleaners is used to illustrate the capability of the proposed approach.

Research on Mining Classification Rules of Spare Parts Based on Grey Rough Set Theory

2011 ◽  
Vol 52-54 ◽  
pp. 1638-1643 ◽  
Author(s):  
Bo Li ◽  
Rui Fang Zhang
Keyword(s):  
Inventory Management ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Spare Parts ◽  
Classification Rule ◽  
Classification Rules ◽  
Traditional Classification ◽  
Classification Management

Mining classification rule of spare parts is very important for inventory management. In traditional classification work of spare parts, the attributes of spare parts were used as a standard to extract classification rules, but it was hard to know the influence of every attribute of spare parts, and which one should be considered, because the attributes of spare parts had many species. So it was necessary for inventory management to mine classification rules of spare parts. Because the values of many attributes of the spare parts are in form of the range of data, the grey rough set theory was borrowed to mine the classification rules in this paper. Firstly, the mining classification rules model of spare parts was built by the grey rough set theory. Then the attributes of spare parts were summarized, and the steps of mining data samples and the mining classification rules of spare parts were introduced respectively. Finally, case study from the classification management of the spare parts of a maintenance factory shows that the proposed mining classification rules model of spare parts based on grey rough set theory can reduce the unnecessary attributes of spare parts without affecting the results of classification.

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