Analysis Of HSK Achievement Based On Data Mining

With the increased popularity of China and Chinese,HSK,a standardized test of Chinese language proficiency for non-native Chinese language speakers,is expanding on a larger and larger scale and amasses immense data of the exam results.Traditional data analysis,which only accesses superficial information,fails to identify the internal association and information hidden in the data.Data mining technology,however,is designed to determine the useful association and regularity underlying the database.This paper studies the data mining of HSK results with the purpose to identify various elements and their interactions that may influence the oversea students in their study of Chinese.First,the author introduces important data mining concepts related to association rule mining.Next,the author elaborates in detail Apriopri algorithm,which is typical of association rule,including the procedures and implementation of the algorithm.Following,the author introduces the application of Apriori algorithm in mining the data of HSK and studies the approach to pre-process the data of HSK dated from 2011 to 2015(the source of the data is the school where the author works).The author compiles the procedures to access and mine the result of HSK,thereby identifying the association rule.Besides,the author makes an analysis of the factors that are associated with the test result,offering guidance to the teaching of Chinese to overseas students.