【正文】 I 畢業(yè)論文 數(shù)據(jù)挖掘算法在銀行客戶細(xì)分中的應(yīng)用 目錄 1 前言 ...............................................................................................................................................................1 問題的由來 .......................................................................................................................................1 國(guó)內(nèi)外研究現(xiàn)狀 ..............................................................................................................................1 主要內(nèi)容和創(chuàng)新點(diǎn) ..........................................................................................................................2 2 數(shù)據(jù)挖掘與商業(yè)銀行客戶細(xì)分 .................................................................................................................3 客戶細(xì)分 ...........................................................................................................................................3 客戶細(xì)分的概述 ...................................................................................................................3 銀行客戶細(xì)分在客戶關(guān)系管理中的意義 .........................................................................4 數(shù)據(jù)挖掘 ...........................................................................................................................................4 數(shù)據(jù)挖掘的概述 ...................................................................................................................4 數(shù)據(jù)挖掘在客戶關(guān)系管理中的應(yīng)用途徑 .........................................................................6 3 數(shù)據(jù)挖掘方法在銀行客戶細(xì)分中的應(yīng)用 ................................................................................................6 數(shù)據(jù)挖掘的一般過程 ......................................................................................................................6 客戶分類指標(biāo)的建立 ......................................................................................................................7 客戶數(shù)據(jù)的選擇和準(zhǔn)備 ..................................................................................................................8 數(shù)據(jù)選擇 ...............................................................................................................................8 數(shù)據(jù)預(yù)處理 ...........................................................................................................................8 數(shù)據(jù)轉(zhuǎn)換 ...............................................................................................................................9 數(shù)據(jù)挖掘 ........................................................................................................................................ 10 數(shù)據(jù)挖掘使用的算法 ........................................................................................................ 10 Kmean 算法的挖掘過程 ................................................................................................. 11 Kmean 算法的數(shù)據(jù)挖掘的結(jié)果 .................................................................................... 13 層次聚類算法的挖掘過程 ............................................................................................... 14 層次聚類算法的數(shù)據(jù)挖掘結(jié)果 ....................................................................................... 16 解釋與評(píng)估， 結(jié)果轉(zhuǎn)換 ............................................................................................................... 18 對(duì)數(shù)據(jù)挖掘的結(jié)果進(jìn)行解釋和評(píng)價(jià) ............................................................................... 18 挖掘結(jié)果轉(zhuǎn)換 .................................................................................................................... 18 知識(shí)運(yùn)用 ........................................................................................................................................ 19 II 4 結(jié)束語(yǔ) ........................................................................................................................................................ 20 參考文獻(xiàn) ...................................................................................................................................................... 21 致謝 ............................................................................................................................................................... 22 附錄一： Kmean 算法聚類結(jié)果 ............................................................................................................... 23 附錄二：層次聚類法個(gè)案聚類結(jié)果 ......................................................................................................... 25 附錄三：層次聚類法變量聚類結(jié)果 ......................................................................................................... 27 III 數(shù)據(jù)挖掘算法在銀行客戶細(xì)分中的應(yīng)用 專 業(yè)： 信息管理與信息系統(tǒng) 摘 要 :隨著改革開放的到來經(jīng)濟(jì)的迅速發(fā)展和騰飛，信息技術(shù)快速興起的和金融行業(yè)的蓬勃發(fā)展，企業(yè)從客戶關(guān)系管理中獲得大量的客戶信息，但是如 何利用好這些珍貴的戰(zhàn)略資源，并通過這些資源對(duì)客戶進(jìn)行分類、保持和發(fā)展，已成為決定商業(yè)銀行在競(jìng)爭(zhēng)激烈的行業(yè)中獲得成功的關(guān)鍵。 面對(duì)這些亟待解決的問題，利用數(shù)據(jù)挖掘算法在客戶關(guān)系管理中對(duì)客戶進(jìn)行細(xì)分無疑是很好的選擇。通過聚類分析能快速的為銀行進(jìn)行客戶分類，并針對(duì)每一客戶群體實(shí)施具體的客戶關(guān)系管理策略和市場(chǎng)營(yíng)銷策略，用最少的成本，為客戶帶來最合適的服務(wù)，并為企業(yè)創(chuàng)造最高的價(jià)值。 本文通過對(duì)銀行客戶細(xì)分的問題由來進(jìn)行闡述，并對(duì)研究文獻(xiàn)進(jìn)行綜述，結(jié)合數(shù)據(jù)挖掘算法進(jìn)行銀行客戶細(xì)分。文章闡明客戶細(xì)分的重要意義和作用，介紹 了數(shù)據(jù)挖掘的算法和客戶細(xì)分的方法，選取人口特征和行為特征的相關(guān)變量分別采用 Kmean算法和層次聚類法對(duì)銀行客戶進(jìn)行數(shù)據(jù)挖掘，得出個(gè)案的聚類結(jié)果和變量的聚類結(jié)果，并將數(shù)據(jù)挖掘結(jié)果轉(zhuǎn)換成具有實(shí)用價(jià)值知識(shí)，最后將結(jié)果轉(zhuǎn)換成客戶細(xì)分方式和營(yíng)銷策略，為銀行決策提供支持。 關(guān)鍵詞： 客戶細(xì)分；數(shù)據(jù)挖掘；聚類分析 The Use of Date Mining Algorithm in the Customer Segmentation of Bank Major: Information Management amp。 Information System Abstract: With the development of our country’s economic and the reforming and opening up policy, the information technology and the financial sector develop faster than before,pan ies can get a large scale of customer information from customer relationship management. While how to make full use of these precious resources, divide customer into different clusters, keep and develop customers through these resources, the problem has been the key factor of winning succ ess of the intense petition of mercial banks. Face to these to be solved problems, date mining is a good choice for managers to make cust omer segmentation. It can do customer