DocumentCode :
2754902
Title :
Notice of Retraction
Application of Classification Algorithm Based on Association Rules in the Labor Market
Author :
Wang Guangming ; Wang Xiaoliang
Author_Institution :
Coll. of Comput. Sci. & Inf. Eng., Zhejiang Gongshang Univ., HangZhou, China
fYear :
2009
fDate :
5-6 Dec. 2009
Firstpage :
255
Lastpage :
258
Abstract :
Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

The classification algorithm based on association rules (CBA) has a good performance in many data sets. At present, there are not many people using classification algorithm to analyze the data of labor market. In this paper, we use CBA algorithm to classify the dataset of the labor market. Then we, by using classification results, make a deeper research to submit a more rational calculation of the registered urban unemployment rate and put forward decision support for the labor market. The experimental results show that CBA algorithm has a high accuracy for the classification of labor market dataset. The research what we do brings a new idea for the macroeconomic analysis and decision support of labor market.
Keywords :
classification; data mining; CBA algorithm; association rules; classification algorithm; decision support; labor market dataset; macroeconomic analysis; urban unemployment rate; Association rules; Classification algorithms; Data analysis; Data mining; Educational institutions; Government; Humans; Itemsets; Management information systems; Unemployment; association rules; classification; classification based on association rules; decision support; labor market; registered urban unemployment rate;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
E-Learning, E-Business, Enterprise Information Systems, and E-Government, 2009. EEEE '09. International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-0-7695-3907-2
Type :
conf
DOI :
10.1109/EEEE.2009.90
Filename :
5359360
Link To Document :
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