scholarly journals Research on the Novel Weighted Fuzzy Clustering Algorithm based on Fuzzy Sets and Rough Set Theory

2015 ◽  
Author(s):  
Chen Liwei
Keyword(s):  
Fuzzy Sets ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
The Novel ◽  
Fuzzy Clustering Algorithm

Financial Distress Study Based on PSO K-Means Clustering Algorithm and Rough Set Theory

2013 ◽  
Vol 411-414 ◽  
pp. 2377-2383 ◽  
Author(s):  
Peng Wu ◽  
Cheng Liu
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Financial Distress ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Financial Indicators ◽  
Financial Status ◽  
Level Data ◽  
Research Objects

The traditional financial distress method normally divided samples into two categories by healthy and bankruptcy. And the financial indicators are typically chosen without using a systematic and reasonable theory. To be more realistic, this paper selected all the companies in a certain industry as the research objects. Twenty-one financial indicators were primarily chosen as the condition attributes, reduction set was obtained by matrix reduction identification based on rough set theory. Then PSO-based clustering algorithm K-means was used to divide subjects into 5 categories of different financial status. The decision-making table was formed with the reduction set using the classification as a decision attribute. Finally, we tested the reasonableness of the classification and generated early warning rules together with rough set theory to evaluate the financial status of listed companies. The results showed that PSO-based K-means algorithm was able to reasonably classify companies, at the same time to overcome the subjective impacts in the artificial measure of financial crisis level. Data generated using this method agreed with the rough set theory for up to 87.0%, thus proving this method to be effective and feasible.

scholarly journals A Novel Research on Rough Clustering Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Tao Qu ◽  
Jinyu Lu ◽  
Hamid Reza Karimi ◽  
E Xu
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Feature Vector ◽  
Space Distribution ◽  
Data Space ◽  
Vector Addition ◽  
Decision Attributes ◽  
Data Subject

The aim of this study is focusing the issue of traditional clustering algorithm subjects to data space distribution influence, a novel clustering algortihm combined with rough set theory is employed to the normal clustering. The proposed rough clustering algorithm takes the condition attributes and decision attributes displayed in the information table as the consistency principle, meanwhile it takes the data supercubic and information entropy to realize data attribute shortcutting and discretizing. Based on above discussion, by applying assemble feature vector addition principle computiation only one scanning information table can realize clustering for the data subject. Experiments reveal that the proposed algorithm is efficient and feasible.

scholarly journals Location optimization of biodiesel processing plant based on rough set and clustering algorithm - a case study in China

2020 ◽  
Vol 40 (3) ◽  
pp. 105-115
Author(s):  
Nana Geng ◽  
Yong Zhang ◽  
Yixiang Sun
Keyword(s):  
Robust Optimization ◽  
Set Theory ◽  
Rough Set ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Raw Material ◽  
Processing Plant ◽  
Waste Oil ◽  
Weighting Method ◽  
Processing Plants

Biofuel has an important role in alleviating the environmental pollution problem. More attention has been paid to optimization of biofuel supply chain in recent years. In this paper, a scientific, rational and practical biodiesel processing plant location with waste oil as the raw material was proposed in order to provide a theoretical basis for guiding the planning and management of restaurants, waste oil collection points, and processing plants. Considering the merits and demerits of the subjective and objective weighting methods, this paper proposes a new weighting method which is namely the combination of rough set theory and clustering algorithm. It then verifies the location results with a plant carbon emission. At last, this paper analyzes the location of biodiesel processing plant in the Yangtze River Delta of China and finds that the precision has been greatly improved with the new method comparing the RMSE and the R2 of the Delphi method with the improved rough set theory. By using this method, the weights of the influencing factors of biodiesel processing plants are the following: Waste oil supply 0.143, Fixed construction cost factor 0.343, Biodiesel demand 0.143 and Location convenience 0.371. In the comparison between the robust optimization method and the improved rough set theory, it was found that the final location results are the same, all being Jiaxing City. However, the improved rough set theory is much simpler than the robust optimization algorithm in the calculation process.

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