查询结果:   杨鹤标,刘桂兰.基于知识点的多支持度挖掘算法[J].计算机应用与软件,2014,31(7):169 - 172.
中文标题
基于知识点的多支持度挖掘算法
发表栏目
人工智能与识别
摘要点击数
749
英文标题
A MULTI-SUPPORT MINING ALGORITHM BASED ON KNOWLEDGE POINTS
作 者
杨鹤标 刘桂兰 Yang Hebiao Liu Guilan
作者单位
江苏大学计算机科学与通信工程学院 江苏 镇江 212013     
英文单位
School of Computer Science and Telecommunication Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu,China     
关键词
知识点 多支持度 关联规则 度量因子
Keywords
Knowledge points Multi-support Association rules Measure coefficient
基金项目
国家自然科学基金项目(61005017,6120 2110)
作者资料
杨鹤标,教授,主研领域:软件工程,数据挖掘,信息系统集成。刘桂兰,硕士。 。
文章摘要
在知识点关联分析方法中,采用单一支持度阈值挖掘频繁知识点集,存在挖掘效率不高的问题。籍此,提出基于知识点的多支持度挖掘算法。算法的思想:针对网络学习平台特有的背景,引入知识点兴趣度和知识点出错频度两个度量因子,用以定量分析学习过程和测试诊断过程,客观地反映用户的学习情况;然后对两个度量因子的相关度进行计算,发现学习过程与测试诊断过程间的相关性;最后,结合多支持度策略,计算出基于知识点度量背景的多支持度,采用改进的多支持度关联规则挖掘进行频繁知识点集的挖掘。实验表明,改进算法在客观的支持度设定基础上,能有效地挖掘出频繁知识点集。
Abstract
In association analysis method in regard to knowledge points, to use the threshold with single support to mine the frequent knowledge points set has the problem of low mining efficiency. Therefore, in this paper we present a knowledge points-based mining algorithm with multi-support. The concept of the algorithm is that, first in consideration of the particular background of web-based teaching platform, we introduce two measure coefficients of knowledge points: the interestingness and the error frequentness to analysis the learning process and the diagnosis process quantitatively, thus to objectively reflect the learning situation of users. Secondly, we compute the relevancy of interestingness and error frequentness, and find the relevance between the learning process and the diagnosis process. Finally, we figure out the multi-support degree based on knowledge points measurement background in combination with multi-support strategy, then mine the frequent knowledge points sets by using the improved multi-support association rules mining algorithm. Experiments show that the improved algorithm can mine the frequent knowledge points sets effectively through setting objective support degree.
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