Abstract

In order to integrate the cultural concepts of basic social value judgments and make the members of society to form an active participation in the consensus, the study use the multi-form subculture and multi-value conflict form in the society as study aim to analysis of that the heterogeneous culture not only will not deconstruct the society but also will bring society to obtain the compatible capacity towards the same. This study indicates that the cultural inertia safeguard their own interests can easily turn into the fuse of conflict in different cultural patterns interactive cooperation of social management. The subcultures collision of the main of pluralistic society management, collectivism and individualistic culture conflict is the main form of social cultural conflict management, the emergence and impact of different cultures is an important driving force for progress and cultural shape.

Keywords:

Collectivism, cultural conflict, individualism, social management, subculture,

References

1. Del Castillo, M.D. and J. Serrano, 2004. A multistrategy approach for digital text categorization from imbalanced documents. SIGKDD Exp., 6(1): 70-79.
   CrossRef    
2. Denoeux, T., 1995. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybernet., 25: 804-813.
   CrossRef    
3. Doan, S. and S. Horiguchi, 2004. An efficient feature selection using multi-criteria in text categorization. Proceeding of the 4th International Conference on Hybrid Intelligent Systems, Washington, DC, pp: 86-91.
   CrossRef    
4. Forman, G., 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res., 3(Mar.): 1289-1305.
   
5. Joachims, T., 1998. Text categorization with Support Vector Machines: Learning with many relevant features. Proceeding of the European Conference on Machine Learning, pp: 137-142.
   CrossRef    
6. Lewis, D., 1997. Reuters-21578 Text Categorization Test Collection, Dist. 1.0, Retrieved from: AT&T Labs-Research.
   Direct Link
7. Rogati, M. and Y. Yang, 2002. High-performing feature selection for text classification. Proceeding of the 11th ACM International Conference on Information and Knowledge Management, pp: 659-661.
   CrossRef    
8. Sebastiani, F., 2002. Machine learning in automated text categorization. ACM Comp. Surv., 34(1): 1-47.
   CrossRef    
9. Shafer, G., 1976. A Mathematical Theory of Evidence. Princeton University Press, Princeton, New Jersey.
   PMCid:PMC2490625    
10. Smets, P. and R. Kennes, 1994. The transferable belief model. Artif. Intell., 66: 191-234.
    CrossRef    
11. Wang, H. and B. David, 2004. Extended k-nearest neighbours based on evidence theory. Comp. J., 47: 662-672.
    CrossRef    
12. Yang, Y. and J.O. Pedersen, 1997. A comparative study on feature selection in text categorization. Proceeding of the 14th International Conference on Machine Learning, Nashville, pp: 412-420.
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.