Teaching affair data matrix weighted positive and negative pattern mining method and system based on correlation

Abstract

The invention provides a teaching affair data matrix weighted positive and negative pattern mining method and system based on correlation. The teaching affair data matrix weighted positive and negative pattern mining method comprises the following steps of pre-treating teaching affair data by using a teaching affair data pre-treating module; generating a subject candidate item set by using a subject candidate item set generation module; obtaining a subject negative item set by using a subject frequent item set and a negative item set generation module according to an item set supporting expect and calculating a candidate item set support degree; comparing with a minimum support degree threshold value to obtain a matrix weighted subject frequent item set and a negative item set; generating whole proper subsets of the subject frequent item set and the negative item set by utilizing a subject positive and negative correlation rule generation module, and calculating the comparison and the confidence coefficient of the item sets; comparing with a minimum confidence coefficient threshold value to obtain a matrix subject positive and negative correlation rule pattern; and displaying the subject positive and negative correlation rule pattern to a user by using a subject positive and negative correlation pattern display module. The teaching affair data matrix weighted positive and negative pattern mining method and system can be applied to a teaching affair information management system and can be used for mining the subject correlation rule pattern which is closer to a real pattern; the pattern can provide scientific evidences to educational transformation, teaching affair administration and education decision making.

Description

technical field [0001] The invention belongs to the field of educational data mining, and specifically relates to a correlation-based educational affairs data matrix weighted positive and negative pattern mining method and its mining system. Pattern mining has important application value and broad application prospects, and its patterns can provide scientific basis for teaching reform, educational management and decision-making. Background technique [0002] Existing research on association rule pattern mining and its application in the field of educational informatization mainly focus on the following aspects. [0003] (1) Research on positive and negative association pattern mining methods: This is a traditional association pattern mining method, and its typical method is the Apriori method (R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in large database[ C] / / Proceeding of 1993 ACM SIGMOD International Conference on Management of Data...

AI Technical Summary

Problems solved by technology

The defect of the traditional association pattern mining method is that it does not consider the different importance of the items (that is, the items have different weights)

The defect of weighted association mode mining is: only consider the different weights between items, and do not consider the situation that items also have different weights in each transaction record

Existing weighted association pattern mining techniques cannot be used for matrix weighted association pattern mining

Since 2003, the matrix weighted association pattern mining method was proposed (Tan Yihong, Lin Yaping. Mining of fully weighted association rules in the vector space model [J]. Computer Engineering and Application, 2003(13): 208-211; Huang Mingxuan, Yan Xiaowei, Zhang Shichao .Pseudo-relevance feedback query expansion based on matrix-weighted association rule mining[J].Software Journal,2009,20(7):1854-1865.), but these mining algorithms cannot solve the mining problem of matrix-weighted negative association patterns

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