Key Data Optimization Mining in Massive Student Database Model

Abstract:

In the process of massive student data mining using traditional method, special words and related characteristics were used as mining objects. The concealment and feature of deliberately camouflaged of information made it is difficult for mining model to form an effective cluster centers, which reduced the accuracy of information mining. Hence an optimized data mining method was proposed. According to the degree of generalization and fuzziness of the feature words of student, the threshold of mining information was set, which avoided the effects of redundant information, thus the efficiency of mining was improved. The experimental results showed that using the improved algorithm to perform information mining in massive student database could effectively improve mining efficiency.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 1466-1469
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.1466
• Online since: November 2014
• Authors: Zhen Chao Wang*
• Keywords: Database, Key Data, Mass, Model

References

[1] Xiao Zhiliang. Database Efficient Mining Method Research Based on the Data Skew Relational [J]. Bulletin of Science and Technology, 2014, 30(2): 53-55.

[2] Yu Guanghua. Security Control of IPv6 Network Access Based on Data Mining With Different Device [J]. Bulletin of Science and Technology, 2014, 30（2）: 74-76.

[3] Song Yu, He Xiaoli. A Mail Network Community Partition Mining Algorithm Based on Log Clustering [J]. Bulletin of Science and Technology, 2014, 30（2）: 96-98.

[4] Li Xiaobo. How to effectively utilize genetics algorithm and neural network to mine the data of Steam turbine [J]. Technology Innovation and application, 2014, (19): 67-67.

[5] Jiang Dongsheng. The Application of Association Rules in the Management of Books [J]. Journal of Henan University (natural science), 2014, (2): 148-148.