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研究生:李紹綸
研究生(外文):Shao-Lun Lee
論文名稱:知識發掘在信用卡之應用
論文名稱(外文):Data Mining for Credit Card System
指導教授:蔣 定 安
指導教授(外文):Ding-An Chiang
學位類別:碩士
校院名稱:淡江大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:中文
論文頁數:81
中文關鍵詞:資料發掘信用卡機器學習知識攫取
外文關鍵詞:data miningcredit cardmachine learningknowledge acquisition
相關次數:
  • 被引用被引用:19
  • 點閱點閱:278
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:6
資料發掘(Data Mining)對於知識擷取(Knowledge Acquisition)而言,是一項非常重要課題。利用資料發掘(Data Mining)我們就可以在大量的資料中,區別隱微難見與毫無意義的資料,並且找到長期趨勢的變化。
根據Meta Group的調查報告,在美國已有超過70%的Fortune 1000大企業,即將進行資料發掘專案,目的是要藉此整合企業內部的資料庫,將經過篩選、整理、彙總過的資料,提供給企業高階主管做決策用。根據對資料發掘使用者的調查顯示,一個成功的資料發掘,其投資報酬率在300%到600%之間,它對企業競爭力的提升的助益可見一般。
本論文主要是運用決策樹(Decision Tree) 和分類法則(Classification Rules)將資料庫中的資料擷取出來,從中得到有用的法則來表達知識。論文中分為兩部分,第一部分是以C4.5演算法為主,從資料庫中選擇出所要分析之欄位─類別欄位(Classes field)和屬性欄位(Properties field),並將其結果以簡單的條件式IF-THEN-ELSE語法或以清晰的樹狀圖來描述此一知識。第二部分則以統計圖表為主,透過SQL指令將資料加以分類,再以統計圖表來呈現此一知識。
本論文實作部分是以Visual Basic和Visual C++程式語言為開發工具,以信用卡資料為分析之對象,藉此瞭解一般信用卡持有人之消費行為模式。
Data mining is very important for knowledge acquisition. By data mining, we can filter out insignificant information and hidden messages from the massive data, and therefore see the long-term trend.
According to a study by Meta Group, over 70% of Fortune’s top 1000 enterprises are conducting data mining to organize the company’s database. The sorted information offers as important reference in executive decision-making process. In a research aiming at data mining users, the result shows that a successful data mining’s investment return rate is 300% to 600%. The role in increasing enterprise’s competition power is crucial.
This thesis is proposed by using Decision Tree and Classification Rules to obtain significant information from the database. The information is further analyzed to become consistent rules, and then knowledge. There are two parts to this thesis. The first part is based on C4.5 Algorithms. The Classes field and Properties field of the chosen information are taken from the database. The result of the analogy are expressed with the if-then-else method or clear tree diagrams. The second part is mainly statistics, which are classified through SQL command, and charted out to present knowledge.
The case study of this thesis is developed by Visual Basic and Visual C++ program language. Credit card holders are the targets. The credit card data is carefully studied and in result we understand customer behaviors.
第一章 緒論……………………………………………………………………1
1.1 前言…………………………………………………………… 1
1.2 研究動機……………………………………………………… 2
1.3 研究目的……………………………………………………… 4
1.4 研究架構……………………………………………………… 6
第二章 背景介紹………………………………………………………………7
2.1 何謂資料發掘………………………………………………… 7
2.1.1 資料發掘的演進…………………………………7
2.1.2 資料發掘的過程…………………………………9
2.1.3 資料庫查詢與資料發掘…………………………9
2.1.4 專家系統與資料發掘………………………… 10
2.1.5 資料發掘的技術……………………………… 12
2.1.6 資料發掘的種類……………………………… 12
2.1.7 資料發掘的工具……………………………… 16
2.2 何謂機器學習…………………………………………………17
2.2.1 人類的學習…………………………………… 18
2.2.2 機器的學習…………………………………… 19
2.2.3 機器學習模式………………………………… 22
2.3 分析工具介紹…………………………………………………25
2.3.1 查詢及報表…………………………………… 25
2.3.2 線上分析處理………………………………… 26
2.3.3 決策資訊系統………………………………… 29
第三章 相關研究…………………………………………………………… 31
3.1 相關演算法介紹………………………………………………31
3.2 決策樹方式推導及法則方式推導……………………………32
3.2.1 決策樹方式推導……………………………… 32
3.2.2 法則方式推導………………………………… 33
3.2.3 決策樹和法則的轉換………………………… 34
3.3 非遞增式推導及遞增式推導…………………………………36
3.3.1 非遞增式推導………………………………… 36
3.3.2 遞增式推導…………………………………… 37
3.4 由上而下推導及由下往上推導………………………………38
3.5 ID3演算法…………………………………………………… 39
3.6 C4.5演算法……………………………………………………42
3.6.1 C4.5trees………………………………………42
3.6.2 C4.5rules………………………………………43
第四章 企業如何導入資料發掘…………………………………………… 44
4.1 資料發掘的角色………………………………………………44
4.2 營運系統資料庫與資料發掘…………………………………46
4.3 資料發掘的導入方法…………………………………………48
4.3.1 資料分析及規畫階段………………………… 48
4.3.2 系統設計階段………………………………… 49
4.3.3 系統施行及建構階段………………………… 50
4.3.4 施行後階段…………………………………… 50
4.4 注意事項………………………………………………………50
第五章 信用卡分析………………………………………………………… 53
5.1 資料發掘引擎…………………………………………………56
5.2 圖形顯示工具…………………………………………………61
5.2.1 資料預處理…………………………………… 62
5.2.2 年度消費金額分析…………………………… 65
5.2.3 年度請卡人數分析…………………………… 67
5.2.4 持卡人年齡分析……………………………… 69
5.3 實作結果………………………………………………………70
第六章 結論………………………………………………………………… 73
參考文獻………………………………………………………………………75
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