Affiliations: [a] School of Computer Science, Xianyang Normal University, Xianyang, China
| [b] Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
Correspondence:
[*]
Corresponding author. Shuying Liu, School of Computer Science, Xianyang Normal University, Xianyang 712000, China. E-mail: lsynate@163.com.
Abstract: The traditional data query algorithm based on clustering strategy library ignores the association features of social network data, characteristic data acquisition exist a large number of redundant features and frequent relationship among features is low, resulting in the social network data query efficiency and the accuracy is poor, so a fast query algorithm for social network data based on fuzzy degree function based on association features is proposed, it is based on Apriori algorithm for data association feature mining of social network to obtain the maximum frequent association feature set; for association feature preprocessing, it reduce the maximum frequent association feature set by feature dimension reduction and de redundancy algorithm, to obtain better social network maximal frequent associated feature set; when using fuzzy function to query social network data quickly, it uses data of a single gene ambiguity function to build a fast data query diagram, input the best frequent feature set of social network, and output the query results of social network data with the highest priority. The experimental results show that the proposed algorithm has the advantages of high efficiency and high accuracy in social network data query.
Keywords: Association features, apriori algorithm, social network data, data set, maximum frequent association features, ambiguity function, query algorithm
