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題名：應用資料採擷探究電信資料異常之研究
書刊名：輔仁管理評論
作者：翁頌舜 引用關係／鄭富山
作者(外文)：Weng, Sung-shun／Cheng, Fu-shan
出版日期：2002
卷期：9:3
頁次：頁77-109
主題關鍵詞：資料採擷；知識探索；類神經網路；電信欺詐；Data mining；Knowledge discovery；Neural networks；Telecommunication fraud
原始連結：連回原系統網址new window
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  • 被引用次數被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:2
  • 共同引用共同引用:0
  • 點閱點閱:13
　　　　　資料採擷（Data Mining）是近年來資料庫應用領域中相當熱門的議題。資料採 擷一般是指在資料庫中，利用各種分析方法與技術，將過去企業所累積的大量歷史資料， 進行分析、歸納與整合等工作，以粹取出有用的資訊，找出使用者有興趣的樣式（ Interesting Patterns），提供企業管理階層作為訂定決策的依據。目前，無論是零售業 、百貨業、電子商務公司、金融機構、電信業、網站管理或醫學診斷等，都已經逐漸體認 到資料採擷的重要性，因此也開始積極從事資料採擷的工作，以為企業創造出真正的價值 。然而上述都傾向於從過去大量的歷史資料中去作分析，在現實生活的應用上，有些資訊 是需要即時告知管理者。例如：電話盜撥、網路干擾、信用卡盜刷等，藉由即時告知以將 損失降至最低；而這些異常的情況可能會經常改變，因此要如何應用資料採擷的技術，來 完成一個具有即時性與適應或（Adaptive）的系統，便成為本研究主要的目標。本研究以 電信資料為實驗環境，應用熱力學中的熵函數（Entropy）來作為評估資料庫資訊含量的重 要指標，並利用類神經網路（Neural Networks）的技術，將標示出的正常與異常資料當作 輸入資料，經由不斷地訓練與學習後，期望能夠準確地找出各種異常的情況，以幫助電信 企業管理者做出最佳的決策，為企業謀得最大的利潤。
　　　　　In recent years, data mining is one of the top issues in the field of database applications. Data mining generally means that it utilizes various kinds of methods and techniques to mine data. It analyzes, generalizes, and integrates the past, accumulated and large quantity of historical information to find out the interesting patterns and pick out useful information as the basis of decision making processes for business executives. No matter in categories of retailing, electronic commerce, finance, telecommunications, web management, medical diagnosis, or others, people have already recognized the importance of data mining gradually. Therefore, they begin to dedicate to data mining aggressively for creating the real values of the enterprises. However, as stated above, data mining tends to analyze the large quantity of historical data. But in order to apply it in the real world, some information, such as telephone frauds, network interruption, credit fraud and so on, is needed to let the company know in time for minimizing the possible loss. But these abnormal situations may change frequently. How to apply data mining techniques to develop a real time and adaptive system is the main goal of this thesis. This research is based on the telecommunication data and uses the "Entropy" theory of Thermodynamics as the main guide for appraising the information capacity in the databases. We use the marked normal and abnormal data as the input of neural networks. Through the iterative process of training and learning of neural networks, we wish to find out abnormal situations precisely in order to help the business executives making the best strategy to earn the maximum profits for enterprises.
期刊論文
1.Cercone, Nick、Cai, Yandong、Han, Jiawei(1993)。Data-Driven Discovery of Quantitative Rules in Relational Databases。IEEE Transactions on Knowledge and Data Engineering，5(1)，29-40。  new window
2.Novakovic, Zaga、柳林緯(1999)。是誰偷偷偷走我的心：淺談行動電話盜打之現況與因應對策。臺灣通訊雜誌，70，128-132。  延伸查詢new window
3.柳林緯(1998)。最愛是你：淺談GSM行動電話標準。臺灣通訊雜誌，60，118-123。  延伸查詢new window
4.賴德謙(1998)。電信經營業者的痛--電話盜撥。臺灣通訊雜誌，59，92-95。  延伸查詢new window
5.Shawe-Taylor, J.、Howker, K.、Burge, P.(1999)。Detection of Fraud in Mobile Telecommunications。Information Security Technical Report，4(1)，16-28。  new window
6.謝邦昌、葉瑞鈴(20000200)。統計在資料掘取之應用。主計月報，530，67-84。  延伸查詢new window
7.劉青儒(1997)。GSM數位行動電話的現況與展望。新電子科技，131，101-108。  延伸查詢new window
8.Glymour C.、Madigan, D.、Pregibon, D.、Smyth, P.(1997)。Statistical Themes and Lessons for Data Mining。Data Mining and Knowledge Discovery，1(1)，11-28。  new window
9.Han, J.(1998)。Towards on-line analytical mining in large databases。ACM SIGMOD Record，27(1)，97-107。  new window
10.Han, J.、Fu, Y.(1999)。Mining Multiple-Level Association Rules in Large Databases。IEEE Transactions on Knowledge and Data Engineering，11(5)，798-805。  new window
11.Quinlan, J. R.(1987)。Simplifying decision trees。International Journal of Man-Machine Studies，27(3)，221-234。  new window
12.Chen, Ming-Syan、Han, Jiawei、Yu, Philip S.(1996)。Data Mining: An Overview from a Database Perspective。IEEE Transactions on Knowledge and Data Engineering，8(6)，866-883。  new window
13.Quinlan, J. R.(1986)。Induction of Decision Trees。Machine Learning，1(1)，81-106。  new window
14.Fayyad, Usama M.、Piatetsky-Shapiro, Gregory、Smyth, Padhraic(1996)。From Data Mining to Knowledge Discovery in Databases。AI Magazine，17(3)，37-54。  new window
會議論文
1.Burge, P.、Shawe-Taylor, J.、Cooke, C.、Moreau, Y.、Preneel, B.、Stoermann, C.(1997)。Fraud detection and management in mobile telecommunications networks。European Conference on Security and Detection。London, UK。91-96。  new window
2.Bonchi, F.、Giannotte, F.、Mainetto, G.、Pedreschi, D.(1999)。A Classification-Based Methodology for Planning Audit Strategies in Fraud Detection。The Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining。San Diego, CA。175-184。  new window
3.Boukerche A.、Sechi, M.、Notare, M. A.(2000)。Neural Fraud Detection in Mobile Phone Operations。IPDPS 2000 Workshops。  new window
4.Adomavicius, G.、Tuzhilin, A.(1999)。User Profiling in Personalization Applications through Rule Discovery and Validation。The Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining。San Diego, CA：ACM。377-381。  new window
5.Fawcett, T.、Provost, F.(1999)。Activity Monitoring: Noticing Interesting Changes in Behavior。The Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，(會議日期: 1999/08/15-1999/08/18)。San Diego, CA。53-62。  new window
6.Rosset, S.、Murad, U.、Neumann, E.、Idan, Y.、Pinkas, G.(1999)。Discovery of Fraud Rules for Telecommunications-Challenges and Solutions。The Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining。San Diego, CA。409-413。  new window
圖書
1.Kennedy, R. L.、Lee, Y.、Roy, B. V.、Reed, C. D.、Lippmann, R. P.(1998)。Solving Data Mining Problems through Pattern Recognition。Upper Saddle River, NJ：Prentice Hall。  new window
2.Berry, Michael J. A.、Linoff, Gordon S.(1997)。Data Mining Techniques: For Marketing, Sales and Customer Support。New York：John Wiley & Sons, Inc.。  new window
3.Cabena, P.、Hadjinian, P. O.、Stadler, R.、Verhees, J.、Zanasi, A.(1997)。Discovering data mining: From concept to implementation。Prentice Hall。  new window
4.Fayyad, U. M.(1998)。Mining Databases: Towards Algorithms for Knowledge Discovery。IEEE Computer Society Technical Committee on Data Engineering。  new window
5.Cleveland, W.(1993)。Visualizing Data。Summit, NJ：Hobart Press。  new window
6.Devore, J. L.(1995)。Probability and Statistics for Engineering and the Science。New York：Duxbury Press。  new window
7.Ross, S. M.(1990)。A Course in Simulation。New York：Maxwell Macmillan。  new window
8.Fayyad, U.、Piatetsky-Shapiro, G.、Smyth, P.、Uthurusamy, R.(1996)。Advances in Knowledge Discovery and Data Mining。Cambridge, MA：AAAI Press。  new window
9.Piatetsky-Shapiro, G.、Frawley, W. J.(1991)。Knowledge Discovery in Databases。Cambridge, MA：AAAI。  new window
10.Pyle, Dorian(1999)。Data Preparation for Data Mining。Morgan Kaufmann Publishers。  new window
圖書論文
1.Fawcett, T.、Provost, F.(1997)。Adaptive Fraud Detection。Data Mining and Knowledge Discovery。  new window
2.Han, J.、Fu, Y.(1996)。Exploration of the Power of Attribute-Oriented Induction in Data Mining。Advances in Knowledge Discovery and Data Mining。Cambridge, MA：AAAI Press。  new window
 
 
 
 
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