指出了Apriroi算法的不足,并提出改進(jìn)算法:在剪枝侯選項(xiàng)目集的時(shí)候,采用生成頻繁項(xiàng)
目集和刪除具有非頻繁子集候選同時(shí)進(jìn)行的方法來(lái)減少多余子項(xiàng)目集的生成。這樣,既提高了掃描數(shù)據(jù)庫(kù)過(guò)程中的信息獲取率,又及時(shí)刪除超集不是頻繁項(xiàng)目集的項(xiàng)集,進(jìn)一步縮減項(xiàng)集的潛在規(guī)模,提高了頻繁項(xiàng)目集即關(guān)聯(lián)規(guī)則生成的效率。討論了如何利用改進(jìn)的Apriori算法對(duì)評(píng)教系統(tǒng)數(shù)據(jù)進(jìn)行關(guān)聯(lián)規(guī)則挖掘。
關(guān)鍵詞:Apriori 算法;評(píng)教系統(tǒng);關(guān)聯(lián)規(guī)則
Abstract: This paper analyses the lack of Apriori algorithm and presents an improved algorithm. This algorithm adopts a new method to reduce the redundant generation of sub-itemsets during pruning the candidate itemsets, which can form directly the set of frequent itemsets and eliminate candidates having a subset that is not frequent in the meantime. The result shows that it can raise the probability of obtaining information in scanning database and reduce the potential scale of itemsets. It is discussed how to apply an improved Apriori algorithm to exploring association rules of the data in Teaching Evaluation System.
Key words: apriori algorithm; teaching evaluation system; association rule