本文關(guān)鍵詞：兩種改進(jìn)的關(guān)鍵規(guī)則挖掘算法研究與應(yīng)用　出處：《合肥工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 數(shù)據(jù)挖掘 關(guān)聯(lián)規(guī)則 Apriori算法 DHP算法 商品擺放

【摘要】：隨著數(shù)據(jù)庫技術(shù)的快速發(fā)展和應(yīng)用,累積了大量的數(shù)據(jù),如何有效、合理的運(yùn)用這些數(shù)據(jù),發(fā)掘數(shù)據(jù)背后隱藏的知識成為了人們關(guān)注的重點(diǎn)。傳統(tǒng)的統(tǒng)計數(shù)據(jù)分析已難堪大任,而數(shù)據(jù)挖掘作為分析數(shù)據(jù)的一種新的工具受到越來越多的重視,成為信息技術(shù)的熱門研究領(lǐng)域。關(guān)聯(lián)規(guī)則作為數(shù)據(jù)挖掘的一個重要的分支,也得到了長足的發(fā)展,在很多方面已經(jīng)取得了重要的成果,但在面對海量數(shù)據(jù)情況下仍然面臨著很多挑戰(zhàn)。隨著關(guān)聯(lián)規(guī)則的發(fā)展,學(xué)者提供了各種關(guān)聯(lián)規(guī)則挖掘算法,比較經(jīng)典的包括Apriori算法、Fp-Growth算法、DHP算法等。但是結(jié)合計算機(jī)技術(shù)和實(shí)際應(yīng)用場景很多算法都有較大的改進(jìn)空間,因此本文在分析了國內(nèi)外關(guān)聯(lián)規(guī)則研究現(xiàn)狀的基礎(chǔ)上,提出了兩種改進(jìn)的關(guān)聯(lián)規(guī)則挖掘算法。本文的主要工作如下。(1)對數(shù)據(jù)挖掘和關(guān)聯(lián)規(guī)則的基本理論進(jìn)行了分類總結(jié)。并詳細(xì)闡述了Apriori和DHP兩種經(jīng)典的關(guān)聯(lián)規(guī)則挖掘算法。(2)提出了改進(jìn)的Apriori算法——DecBit Apriori算法,DecBitApriori算法將事物數(shù)據(jù)庫轉(zhuǎn)換成十進(jìn)制數(shù)數(shù)據(jù)庫,然后使用與位運(yùn)算計算候選集的支持度。最后通過實(shí)驗(yàn)驗(yàn)證了DecBitApriori算法在運(yùn)行效率上的提高。(3)提出了改進(jìn)的DHP算法——RBTDHP算法,RBTDHP算法使用紅黑樹數(shù)據(jù)結(jié)構(gòu)處理DHP算法散列過程中的沖突,可以讓所有的候選集單獨(dú)計數(shù),避免了DHP算法需要重復(fù)掃描數(shù)據(jù)庫得到候選集的支持度。(4)基于超市實(shí)際購物數(shù)據(jù),使用DecBitApriori算法挖掘關(guān)聯(lián)規(guī)則,然后根據(jù)挖掘出的關(guān)聯(lián)規(guī)則結(jié)果提出了超市商品擺放的相關(guān)建議。
[Abstract]:With the rapid development and application of database technology, accumulated a large amount of data, how to effectively use these data reasonably, explore the knowledge behind the data become the focus of attention. The traditional statistical data analysis and data mining has great embarrassment, as a new tool of data analysis has received more and more attention and become a hot research field of information technology. As an important branch of data mining association rules as, also obtained the considerable development, the important results have been achieved in many ways, but in the face of massive data situation still faces many challenges. With the development of association rules, scholars provide a variety of mining association rules the algorithm, including Apriori algorithm, Fp-Growth algorithm is the classic DHP algorithm. But the combination of computer technology and practical application of the scene many algorithms have changed greatly In space, therefore, based on the analysis of the domestic and foreign research status of association rules, this paper proposes two improved association rule mining algorithm. The main work of this paper is as follows. (1) are classified and summarized the basic theory of data mining and association rules. And expounds the association rules Apriori and DHP two classic the data mining algorithm. (2) proposed the improved Apriori algorithm, DecBit Apriori algorithm, DecBitApriori algorithm will convert the decimal number database transaction database, and then use the support and computing candidate sets. Finally we validate the DecBitApriori algorithm to enhance the efficiency in the operation. (3) proposes an improved DHP algorithm. RBTDHP algorithm, RBTDHP algorithm uses conflict red black tree data structure DHP algorithm hash process, can make all the candidate set separate count, to avoid the need to re DHP algorithm The support of the candidate set is obtained from the complex scan database. (4) based on the actual shopping data of supermarkets, we use DecBitApriori algorithm to mine association rules, and then put forward relevant suggestions for supermarket's commodity placement according to the results of mining association rules.

【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13

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