Expansion Research on K-means Clustering Algorithm Based on Rough Set

2012 ◽  
Vol 4 (10) ◽  
pp. 221-227 ◽  
Author(s):  
Xuexia YANG
Keyword(s):  
Rough Set ◽  
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.

A Clustering Algorithm of E-Learningers Based on Rough Set

2013 ◽  
Vol 756-759 ◽  
pp. 3260-3264
Author(s):  
Yun Hua Wang ◽  
Hui Yan Ke
Keyword(s):  
Rough Set ◽  
Clustering Algorithm ◽  
Dimensional Space ◽  
Teaching Content ◽  
E Learning ◽  
Individualized Teaching ◽  
Low Dimensional

The demand for individualized teaching from E-learning websites is rapidly increasing due to the huge differences existed among E-learning learners. A method for clustering E-learningers based on rough set is proposed. The basic idea of the method is to reduce the learning attributes prior to clustering, and therefore the clustering of E-learningers is carried out in a relative low-dimensional space. Using this method, the E-learning websites can arrange corresponding teaching content for different clusters of learners so that the learners individual requirements can be more satisfied.

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