A Knowledge Rule Mining Method for the Evaluation of Library Service Quality Based on Genetic Algorithm

Abstract:

In this study, the evaluation index system of library service quality is established and the representation method of knowledge rule is analyzed firstly. Then, a knowledge rule mining method for the evaluation of library service quality based on an improved genetic algorithm is proposed. In the algorithm, selection operator, help operator, crossover operator and mutation operator are used to generate new knowledge rules. Knowledge rules are evaluated by their accuracy, coverage and reliability. Experimental results show that this knowledge rule mining method is feasible and valid. It is helpful for us to evaluate the library service quality fairly and objectively.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1588-1592
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1588
• Online since: June 2012
• Authors: Ping Wang, Tai Shan Yan
• Keywords: Evaluation of Service Quality, Genetic Algorithm (GA), Knowledge Rule Base, Knowledge Rule Mining, Library

References

[1] Diao Li-li. Data mining and Combination Learning. Computer Science, 2001, 28(7): 73～78.

[2] Dasarathy, B. V Nearest Neighbor Norms. NN Pattern Classification Techniques. Washington, D.C.: IEEE Computer Society, (1991).

[3] ZIARKO W. Rough sets, Fuzzy Sets and Knowledge Discovery. New York: Springer-Verlag, (1994).

DOI: 10.1007/978-1-4471-3238-7_2

[4] Shi Xiao-Qin. Evaluation for Reader's Satisfaction and Its Strategy in Network Environment. Journal of Shanxi Library, 2007(4): 41～43.

[5] Fan Xiao-Hua, Yuan Yong-Zhi, Li Qi-Ren. Evaluation Indicator System of the Library Service Performance. Library Construction, 2009, (8): 46-50.

[6] Zhang Hong-Mei. Research on the Evaluation Model for Information Service in Foreign Libraries. Information Journal, 2007(8): 40～42.

[7] Wang Xiao-Ping, Cao Li-Ming, Genetic algorithm – theory, application and software realization. Xi'an Jiao tong University Press, Xi'an, (2002).

[8] Thomas Strassner, Markus Busold, Wolfgang A. Herrmann, MM 3 parametrization of four- and five- coordinated rhenium complexes by a genetic algorithm. Journal of Computational chemistry, Vol. 23, (2002).

DOI: 10.1002/jcc.10000

[9] Yan Tai-shan, Cui Du-wu, Tao Yong-qin. A New Evolutionary Neural Network Algorithm Based on Improved Genetic Algorithm and Its Application in Power Transformer Fault diagnosis. The Second International Conference on Bio-Inspired Computing: Theories and Applications, 25-28, (2007).

DOI: 10.1109/bicta.2007.4806406