本文關(guān)鍵詞：應(yīng)用Apriori關(guān)聯(lián)規(guī)則算法的數(shù)據(jù)挖掘技術(shù)挖掘電子商務(wù)潛在客戶，由筆耕文化傳播整理發(fā)布。

內(nèi)容摘要計(jì)算機(jī)技術(shù)與網(wǎng)絡(luò)技術(shù)飛速發(fā)展，使電子商務(wù)顯示出越來越強(qiáng)大的生命力, 因其高效與便捷的特點(diǎn)使人們從傳統(tǒng)的購物方式逐漸轉(zhuǎn)向網(wǎng)絡(luò)購物方式,日益接受其網(wǎng)絡(luò)交易的模式。隨著越來越多的公司進(jìn)入電子商務(wù)界,電子商務(wù)公司如何拓展客戶,從眾多的使用者中發(fā)掘出潛在客戶,對電子商務(wù)公司的發(fā)展是相當(dāng)重要的.基于上述情況,設(shè)計(jì)了將數(shù)據(jù)挖掘應(yīng)用于電子商務(wù)的潛在用戶分析,利用電子商務(wù)系統(tǒng)數(shù)據(jù)庫中的存儲的大量用戶信息及交易數(shù)據(jù),經(jīng)過分類轉(zhuǎn)換之后采用 Apriori 關(guān)聯(lián)規(guī)則算法的數(shù)據(jù)挖掘技術(shù)進(jìn)行挖掘 ,并得到電子商務(wù)用戶之間的關(guān)聯(lián)關(guān)系.通過分析這些數(shù)據(jù)找到電子商務(wù)用戶中的哪些能成為真正使用者或者說是潛在客戶,從而為電子商務(wù)公司的發(fā)展決策提供科學(xué)依據(jù)。數(shù)據(jù)挖掘的過程就是發(fā)現(xiàn)隱藏在各種尚沒有處理的原始數(shù)據(jù)集合中的各種相關(guān)聯(lián)系,并從這些聯(lián)系中提取知識的過程。數(shù)據(jù)挖掘是多種計(jì)算機(jī)相關(guān)學(xué)科相結(jié)合的產(chǎn)物，其包含了數(shù)據(jù)庫技術(shù)、計(jì)算機(jī)機(jī)器自主學(xué)習(xí)、數(shù)據(jù)統(tǒng)計(jì)分析、行為模式識別、人工神經(jīng)網(wǎng)絡(luò)等等學(xué)科。由于其具有很高的商業(yè)使用價(jià)值，同時適合應(yīng)用的范圍極為廣泛，所以目前數(shù)據(jù)挖掘的相關(guān)研究已成為研究的重點(diǎn)之一。論文首先對電子商務(wù)的現(xiàn)狀進(jìn)行了分析，發(fā)現(xiàn)其存在的問題，即海量的數(shù)據(jù)信息無法得到有效利用。之后提出了采用數(shù)據(jù)挖掘?qū)?shù)據(jù)信息進(jìn)行挖掘，從而找到電子商務(wù)中的潛在客戶的這一個設(shè)想。接著對數(shù)據(jù)挖掘、關(guān)聯(lián)規(guī)則等各種相關(guān)知識進(jìn)行了闡述，，并對它們的發(fā)展情況，以及國內(nèi)外數(shù)據(jù)挖掘的現(xiàn)狀做了簡單的綜述。分析了電子商務(wù)平臺中數(shù)據(jù)挖掘系統(tǒng)發(fā)展現(xiàn)狀，以及應(yīng)用數(shù)據(jù)挖掘會給電子商務(wù)帶來何種優(yōu)勢。然后重點(diǎn)研究了關(guān)聯(lián)規(guī)則經(jīng)典頻繁集算法(Apriori 算法)的原理，以及如何將挖掘算法應(yīng)用于電子商務(wù)系統(tǒng)中，針對它的缺點(diǎn)，提出了一種改進(jìn)算法。從而在挖掘事務(wù)數(shù)據(jù)庫時，將要查詢的數(shù)據(jù)表取出放入內(nèi)存中，從而使以后每次掃描時不需要再訪問數(shù)據(jù)庫，而是直接訪問內(nèi)存，從而使速度增快；能減少對數(shù)據(jù)庫的訪問次數(shù)，同時建立輔助表，來幫助減少掃描頻繁項(xiàng)集節(jié)點(diǎn)次數(shù)，從而提高整個挖掘的效率。最后介紹了將 Apriori 算法應(yīng)用到電子商務(wù)，設(shè)計(jì)了潛在客戶分析系統(tǒng)，通過對源數(shù)據(jù)庫中客戶各種相關(guān)基本信息的提取、整合，之后進(jìn)行挖掘分析來得出結(jié)論，哪些客戶能成為潛在的客戶。關(guān)鍵詞:數(shù)據(jù)挖掘,關(guān)聯(lián)規(guī)則,Apriori 算法,潛在客戶挖掘FIND THE POTENTIAL CUSTOMERS OF THEE-COMMERCE BY DATA MINING TECHNOLOGYAbstractE-commerce shows more and more st

rong vitality because of the Computer andthe internet technology has rapid development. People’s traditional shopping way haschanged. Shopping from internet has already became the way that the people willaccept. As More and more companies enter the e-commerce industry, the importantof the Electronic commerce’s development is that how to develop customer and howto discover potential customers from the users.On the above situation, we analysis potential user in e-commerce by Data mining.We use the large number of user information and transaction data which is stored inthe electronic commerce system database. Through the classification after theconversion using Apriori algorithm of association rules data mining technology wasobtained, and get the relation between the users of e-commerce. We analysis this datain order to find the users of e-commerce which can become a potential customers. Sothat it can provide scientific basis for the electronic commerce’s development anddecision-making.Data mining is that found the original data in the associations been hidden in orcorrelations between process and knowledge acquisition. Data mining is thetechnology of database, artificial intelligence, machine learning, statistical analysis,fuzzy logic, pattern recognition, artificial neural network, and multiple disciplinescombined product. Data mining has become one of research hot spots because of itswide application and high commercial value of current.Firstly, I expounded data mining, association rules and other related knowledge.In the current, association rules of data mining are the one of main mode thatresearches with. It focuses on the identification the data which in different areas ofcontact between. It finds the support and confidence thresholds of the multipledomain dependencies which are given in satisfy. I make comprehensive summary thebackground and development situation on data mining as well as the present situationof the domestic and foreign data mining system. I Analysis the data mining systemwhich faced and needed to solve the problems, and development trend of the future.The paper put the stress on the theory of Apriori and the its application toelectronic commerce system. According to its shortcomings, this paper also putforward some improved algorithm. It is faster and more efficient. It cuts downs thetimes we visit the data-base. Finally the paper introduces the application of thealgorithm to analyze the electronic commerce customers and find the potentialcustomers through analyzing the information of these customers.Finney I describes how to analysis customer with this algorithm and theelectronic commerce. I use the customer basic information extraction and analysis ofmining to find which customers can become potential customers.Key Words: Data mining, Association rules, potential customers, Apriori目錄第一章 引言.........................................

.........................................................................11.1 動機(jī)...................................................................................................................11.2 研究目的...........................................................................................................11.3 國內(nèi)外應(yīng)用研究...............................................................................................21.4 數(shù)據(jù)挖掘研究過程..........................................................................................31.5 論文組織...........................................................................................................4本章小結(jié).................................................................................................................4第二章 數(shù)據(jù)挖掘..........................................................................................................52.1 數(shù)據(jù)挖掘介紹...................................................................................................52.2 挖掘?qū)ο笱芯?..................................................................................................62.3 可挖掘的知識模式...........................................................................................62.4 數(shù)據(jù)挖掘分析方法...........................................................................................72.4.1 分類 （Classification）..........................................................................................72.4.2 估值（Estimation） ................................................................................................82.4.3 預(yù)言（Prediction） .................................................................................................82.4.4 關(guān)聯(lián)規(guī)則（Affinity grouping or association rules）..............................................82.4.5 聚集（Clustering） .................................................................................................92.4.6 數(shù)據(jù)挖掘方法總結(jié)..................................................................................................92.5 電子商務(wù)平臺中的數(shù)據(jù)挖掘技術(shù)應(yīng)用.........................................................102.5.1 找到潛在客戶........................................................................................................102.5.2 實(shí)現(xiàn)客戶駐留........................................................................................................112.5.3 改進(jìn)站點(diǎn)的設(shè)計(jì)....................................................................................................112.5.4 進(jìn)行市場預(yù)測........................................................................................................122.6 應(yīng)用數(shù)據(jù)挖掘技術(shù).........................................................................................12本章小結(jié)......................

.........................................................................................12第三章 關(guān)聯(lián)規(guī)則挖掘以及算法................................................................................133.1 關(guān)聯(lián)挖掘的規(guī)則定義.....................................................................................133.2 關(guān)聯(lián)規(guī)則挖掘的過程.....................................................................................153.3 關(guān)聯(lián)規(guī)則的各種種類.....................................................................................163.3.1 關(guān)聯(lián)規(guī)則可以分為布爾型和數(shù)值型。 ................................................................163.3.2 單層關(guān)聯(lián)規(guī)則和多層關(guān)聯(lián)規(guī)則。........................................................................163.3.3 關(guān)聯(lián)規(guī)則可以分為一維的和多維的....................................................................163.4 關(guān)聯(lián)規(guī)則挖掘的相關(guān)算法.............................................................................173.4.1 Apriori 算法： .......................................................................................................173.4.2 基于劃分的算法:...................................................................................................173.4.3 FP-樹頻集算法:.....................................................................................................173.4.4 APRIORI 關(guān)聯(lián)規(guī)則算法： ...................................................................................173.4.5 Apriori 算法的缺點(diǎn) ...............................................................................................183.4.6 Apriori 算法改進(jìn)的基本思想 ...............................................................................193.5 數(shù)據(jù)挖掘準(zhǔn)備步驟：.....................................................................................193.6 實(shí)例分析.........................................................................................................203.7 具體實(shí)驗(yàn)分析.................................................................................................243.8 Apriori 算法的應(yīng)用 ........................................................................................26本章總結(jié)：...........................................................................................................26第四章．關(guān)聯(lián)規(guī)則在電子商務(wù)平臺中尋找潛在客戶的應(yīng)用..................................274.1 電子商務(wù)概述.................................................................................................274.2 電子商務(wù)未來的趨勢：.................................................................................294.3 潛在客戶的定義.............................................................................................304.4 尋找潛在客戶的目的和意義.....................................

  本文關(guān)鍵詞：應(yīng)用Apriori關(guān)聯(lián)規(guī)則算法的數(shù)據(jù)挖掘技術(shù)挖掘電子商務(wù)潛在客戶，由筆耕文化傳播整理發(fā)布。