【正文】 ............................ 18 關(guān)聯(lián)規(guī)則挖掘的研究方向 .................................................................................. 19 本章小結(jié) .............................................................................................................. 20 第四章 Apriori 算法及其改進(jìn)設(shè)計(jì) ................................... 21 經(jīng)典的 Apriori算法 ............................................................................................ 21 Apriori算法的基本思想 ............................................................................. 21 Apriori算法的核心描述和分析 ................................................................. 21 Apriori算法中規(guī)則的產(chǎn)生 ......................................................................... 23 Apriori算法的舉例演示 ............................................................................. 24 Apriori算法的特點(diǎn)和缺陷 ......................................................................... 26 Apriori算法的現(xiàn)有改進(jìn)技術(shù) ..................................................................... 26 一種新的 Apriori算法改進(jìn)設(shè)計(jì) ........................................................................ 27 改進(jìn)思路 .................................................................................................... 27 Apriori改進(jìn)算法的描述和實(shí)例分析 ......................................................... 28 Apriori改進(jìn)算法的特點(diǎn)和不足 ................................................................. 33 Apriori算法和 Apriori改進(jìn)算法的性能比較 ..................................................... 34 性能分析 .................................................................................................... 34 實(shí)驗(yàn)分析 .................................................................................................... 35 本章小結(jié) .............................................................................................................. 36 第五章 Apriori 改進(jìn)算法在學(xué)生成績管理中的應(yīng)用 ....................... 37 關(guān)聯(lián)規(guī)則挖掘過程 .............................................................................................. 37 關(guān)聯(lián)規(guī)則挖掘在學(xué)生成績管理中的應(yīng)用 .......................................................... 38 問題定義 .................................................................................................... 38 數(shù)據(jù)準(zhǔn)備 .................................................................................................... 38 建立數(shù)據(jù)挖掘模型 .................................................................................... 40 關(guān)聯(lián)規(guī)則的解釋和評(píng)估 ............................................................................ 45 本章小結(jié) .............................................................................................................. 45 第六章 總結(jié)與展望 ............................................... 46 論文總結(jié) .............................................................................................................. 46 展望 ...................................................................................................................... 46 參考文獻(xiàn) ....................................................... 48 攻讀碩士學(xué)位期間公開發(fā)表的論文 ....................... 錯(cuò)誤 !未定義書簽。 Performance Management Abstract Association rule mining is used to find the meaningful connections hidden in large data set, and the connections can be expressed by association rules or frequent itemsets. Currently, the association rule mining has been widely studied and applied, of which Apriori algorithm is one of the most influential mining Boolean association rule algorithms of frequent itemsets. Aiming at the shortings of Apriori algorithm, this thesis proposes an improved algorithm and applies it to mine student performances, thus plays a certain guiding role in curriculum optimization. The main contents of this thesis are as follows: (1)Firstly, it discusses and summaries the data mining technology, and emphasizes the basic concepts and ideas of association rules, and related techniques about frequent itemsets and association rules. (2)Secondly, it studies the Apriori algorithm t horoughly. And present an improved algorithm aiming at the flaws. The algorithm uses the perfect hash function, optimized affairs pression technology, the grouping inquiry counting and not using the pruning directly to produce candidate k itemsets technology and so on. The improved algorithm enhances the efficiency of mining frequent itemsets to a certain extent. At the same time, it confirms the superiority of the improved algorithm by paring the two algorithms from theory and experiment aspect. (3)Finally, it applies the association rules mining to the student performance management. On the foundation of student performance administration module in the original educational administration management system, by applying the improved Apriori algorithm and VB 20xx, it designs a data mining system to mine association rule in the student performance. This system includes four modules: the data gain, the data pretreatment, the association rule mining, and the regular result analyzing. Through mining the student performance, it further confirms the validity and the feasibility of improved Apriori algorithm, and also provides decision support for the teaching management to Optimize curriculum. After the operation of the system, the teaching process was improved, the teaching effect was enhanced and the pass rate was increased. Key words: Data Mining。該系統(tǒng)包括獲取數(shù)據(jù)，數(shù)據(jù)預(yù)處理，關(guān)聯(lián)規(guī)則挖掘和規(guī)則結(jié)果分析四個(gè)模塊。改進(jìn)算法利用了完美哈希函數(shù)，優(yōu)化的事務(wù)壓縮技術(shù)，分組查詢計(jì)數(shù)和不利用剪枝直接產(chǎn)生候選 k 項(xiàng)集等技術(shù)，在一定程度上提升了挖掘頻繁項(xiàng)集的效率。目前，關(guān)聯(lián)規(guī)則挖掘已經(jīng)得到了廣泛的研究和應(yīng)用，其中 Apriori 算法是一種最有影響的挖掘布爾關(guān)聯(lián)規(guī)則頻繁項(xiàng)集的算法。本文針對(duì) Apriori 算法的不足，提出了一種改進(jìn)算法，并將其應(yīng)用于挖掘?qū)W生成績，從而對(duì)優(yōu)化課程設(shè)置起到一定的指導(dǎo)作用。同時(shí)，通過理論和實(shí)驗(yàn)對(duì)兩種算法進(jìn)行了性能比較，驗(yàn)證了改進(jìn)算法的優(yōu)越性。 通過挖掘?qū)W生成績，進(jìn)一步證實(shí)了 Apriori 改進(jìn)算法的有效性和可行性，也為教學(xué)管理人員進(jìn)行課程合理設(shè)置提供了決策支持。 Association Rules。 插圖清單 圖 3 1 費(fèi)力策略示意圖 ............................................................................... 12 圖 3 2 基于支持度的剪枝策略的實(shí)例 .......................................................... 13 圖 3 3 FPgrowth 算法偽