文章基本信息

标题：Assessment, Prediction, Educational data mining, Decision tree, C4.5algorithm, ID3 algorithm
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作者：Sanober Shaikh ; Madhuri Rao
期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2011
卷号：1
期号：6
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In this paper a new mining algorithm is defined based on frequent item set. Apriori Algorithm scans the database every time when it finds the frequent item set so it is very time consuming and at each step it generates candidate item set. So for large databases it takes lots of space to store candidate item set .In undirected item set graph, it is improvement on apriori but it takes time and space for tree generation. The defined algorithm scans the database at the start only once and then from that scanned data base it generates the Trade List. It contains the information of whole database. By considering minimum support it finds the frequent item set and by considering the minimum confidence it generates the association rule. If database and minimum support is changed, the new algorithm finds the new frequent items by scanning Trade List. That is why it’s executing efficiency is improved distinctly compared to traditional algorithm.
关键词：Undirected Item set Graph; Trade List