博碩士論文 965302008 詳細資訊




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姓名 黃若瑜(Juo-yu Huang)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 應用自組織映射圖網路及倒傳遞網路於探勘通信資料庫之潛在用戶
(Applying Self-Organizing Map and Back-Propagation Network to Mining Telecommunication Potential Customers)
相關論文
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摘要(中) 近年來電信市場蓬勃發展,各家業者致力於各種行銷方式,目的是為了吸引更多客戶加入。在眾多的行銷企畫中,雖能夠吸引不同的消費者,但也會耗費業者高成本資源。倘若能以較低的成本達到相同的結果,對業者而言是較為樂見的,而針對不同類型的消費者,以不同的方式進行銷售,就不需要將每筆行銷廣告發送給每位客戶,進而替業者省下一筆可觀的預算。因此,如何找出不同類型的電信用戶則成為本研究的目的,以現有的通信資料庫進行資料探勘,區別出各類型的用戶提供業者參考。
區別客戶類型的研究大多以個人資料及歷史消費紀錄作為研究資料來源。然而,國內電信業者對於申請用戶的資料記錄並不詳細,亦無法確定登記申請者與電話使用者為同一人。因此,本研究採用大量的歷史通訊紀錄為資料來源,應用自組織映射圖網路技術分析用戶行為特性,從中區別各類型的用戶並指出各類別特性,並整合倒傳遞網路技術進行模型建構,以期發展可靠的預測模型。
摘要(英) With the growth of telecommunication market, every Internet Service Provider (ISP) proposes many kinds of marketing strategies to expand their market and gain customers. Although lots of these marketing plans can attract users and increase volume of sales of products, most of these strategies cost much and resource wasted for ISPs. For customized marketing, nowadays, discovering different customer communities through customer profiles and historical consumer behaviors is important and urgent, and it motivates our research.
For promoting specific products to different groups, we collect a large amount of historical data of telecommunication users, and apply state-of-the-art classification methods to categorize different communities of consumer behaviors. Due to the stabilization and efficiency of neural network, we employ self-organizing map and back-propagation network algorithms to generate a prediction model, and utilize it to group different consumer communities, then analyze them from different phases for promotion. From the experimental results, we believe this approach can classify communities efficiently and accurately, and it is workable to support marketing in telecommunication market.
關鍵字(中) ★ 倒傳遞網路
★ 自組織映射圖網路
★ 通信資料探勘
★ 資料探勘
關鍵字(英) ★ Back-propagation network
★ Self-organizing map
★ Data mining
★ Telecommunication data mining
論文目次 目 錄
頁次
中文摘要 …………………………………………………………………………… i
英文摘要 …………………………………………………………………………… ii
目錄 …………………………………………………………………………… iii
圖目錄 …………………………………………………………………………… v
表目錄 …………………………………………………………………………… vii
一、 緒論
1-1 研究背景………………………………………………………………… 1
1-2 研究動機與目的………………………………………………………… 2
1-3 研究方法與流程………………………………………………………… 2
1-4 論文架構………………………………………………………………… 3
二、 文獻探討
2-1 資料探勘方法…………………………………………………………… 5
2-1-1 群聚分析………………………………………………………………… 6
2-1-2 分類分析………………………………………………………………… 7
2-2 類神經網路方法………………………………………………………… 8
2-3 資料探勘於電信業之應用……………………………………………… 10
2-4 資料探勘於潛在用戶發掘之應用……………………………………… 12
三、 探勘潛在用戶之研究分析
3-1 探勘潛在用戶系統架構………………………………………………… 16
3-2 來源資料彙整…………………………………………………………… 17
3-2-1 來源資料………………………………………………………………… 17
3-2-2 分析資料………………………………………………………………… 18
3-3 應用自組織映射圖網路於群聚分析…………………………………… 19
3-3-1 自組織映射圖網路……………………………………………………… 19
3-3-2 參數選擇………………………………………………………………… 23
3-4 應用倒傳遞網路於判別預測分析……………………………………… 24
3-4-1 倒傳遞網路……………………………………………………………… 24
3-4-2 參數選擇………………………………………………………………… 28
四、 實驗結果與分析
4-1 群集網路建構與分析…………………………………………………… 31
4-2 分類網路建構與分析…………………………………………………… 44
五、 結論 51
參考文獻 …………………………………………………………………………… 52
附錄 …………………………………………………………………………… 56
參考文獻 [1]IBM,「資料探挖-找出隱藏在資料庫中的寶藏」,資訊傳真週刊,256期,1997年。
[2]李惠妍,「類神經網路與迴歸模式在台股指數期貨預測之研究」,國立成功大學,碩士在職專班碩士論文,2003年。
[3]高淑珍,「 應用資料探勘於顧客回應模式之研究-以國內A壽險公司為例」,國立成功大學,博士論文,2004年。
[4]高仲仁,「運用類神經網路進行隧道岩體分類,」國立中央大學,碩士論文,2001年。
[5]何明珊,「行動加值服務市場區隔與使用意願之研究」,國立成功大學電信管理研究所,碩士論文,2004年。
[6]邱義堂,「通信資料庫之資料探勘:客戶流失預測之研究」,國立中山大學,碩士論文,2001年6月。
[7]周明輝,「利用類神經網路計算地層參數之研究-雲林地區案例分析」,國立成功大學,碩士論文,2002年。
[8]周君瑞、陳國祥,「不同參數設定對類神經網路預測能力之影響—以產品造形與感性義象的對應關係為例」,工業工程學刊第十九卷,第六期,1~12頁,2002年。
[9]中華電信,中華電信企業資料倉儲系統簡介,中華電信公司,2000年。
[10]陳世凱,「中華電信公司組織變革之研究」,國立中山大學,碩士論文,2003年6月。
[11]陳文華,「應用資料倉儲系統建立CRM」,資訊與電腦,122~127頁,1999年5月。
[12]蔡家昌,「應用決策樹歸納法探討台灣行動電話市場區隔」,國立台北大學,碩士論文,2002年。
[13]葉怡成,類神經網路模式應用與實作,儒林出版社,台灣,1993年。
[14]A.K. Jain, M.N. Murty and P.J. Flynn, "Data Clustering: A Review", ACM Computing Surveys, Vol. 31, No. 3, pp. 264-323, 1999.
[15] A.P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm”, Journal of the Royal Statistical Society. Series B. Vol. 39, No. 1, pp. 1-38, Aug 2005.
[16]B. Daniela, C. Raffaele and T. Brunello, "Kohonen neural networks and genetic classification", Mathematical and Computer Modeling, ISSN: 08957177, Vol. 45, pp. 34-60, January 2007.
[17]B.E. Boser, I.M. Guyon and V.N. Vapnik, “A training algorithm for optimal margin classifiers”, The 5th Annual ACM Workshop on COLT, pp. 144-152, 1992.
[18]C. Phua, et al., “Minority report in fraud detection: Classification of skewed data”, SIGKDD Explorations, 6(1): pp. 50-59, 2004.
[19]Carlos André R. Pinheiro, Alexandre G. Evsukoff and Nelson F. F. Ebecke, "Neural Network to Identify and Prevent Bad Debt in Telephone Companies", IEEE 2005 International Conf. on Data Mining: Data Mining Case Studies Workshop, Paris, France, June 28-July 1, 2009.
[20]Customer Retention Practices : Solutions http://retention.harrisblackintl.com/solutions.
[21]D. Brugger, M. Bogdan, and W. Rosenstiel, "Automatic cluster detection in Kohonen's SOM," IEEE Transactions on Neural Networks, Vol.19(3), pp. 442-459, 2008.
[22]D. Heckerman, “Bayesian networks for knowledge discovery”, in AKDDM, AAAI/MIT Press, pp. 273-306, 1996.
[23]D. Koller, and M. Sahami, “Hierarchically classifying documents using very few words”, Proceedings of the 14th International Conference on Machine Learning, pp.170-178, 1997.
[24]E. Glover, K. Tsioutsiouliklis, S. Lawrence, D. Pennock and G. Flake, “Using Web Structure for Classifying and Describing Web Pages,” In Proc. of the 11th International World Wide Web Conference, pp. 562-569, 2002.
[25]Hao-shan Chien, "The Application of Self-organizing Map to Clustering Type II Telecommunications Business and Customers", Tatung University, Thesis for Master of Science, Department of Computer Science and Engineering, 2005.
[26]Hao-shan Chien, "The Application of Self-organizing Map to Clustering Type II Telecommunications Business and Customers", Tatung University, Thesis for Master of Science, Department of Computer Science and Engineering, 2005.
[27]J. Han, and M. Kamber, Data Mining: Concepts and Techniques, Elsevier, San Francisco, 2006.
[28]J.R. Quinlan, “Induction of Decision Trees”, Machine Learning, Vol. 1, No. 1, pp. 81-106, 1986.
[29]J. Vesanto and E. Alhoniemi, ”Clustering of the self-organizing map”, IEEE Transactions on Neural Networks, 11(3), pp. 586-600, 2000.
[30]Hsiu-Ping Yang , “The Application of Grey Model and Artificial Intelligence Techniques to Drive Revenue Assurance of the Telecommunication Operator”, Tatung University, Thesis for Master of Science, Department of Computer Science and Engineering, 2004.
[31]M. Ankerst, M. Breunign, H.p. Kriegel and J. Sander, “OPTICS: Ordering points to identify the clustering structure,” In Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’99), pp. 49-60, Philadelphia, PA, June 1999.
[32]M. Ester, H.P. Kriegel and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” In Proc. of the Second International Conference on Knowledge Discovery and Data Mining (KDD 96), pp. 226–231, 1996.
[33]O.A. Abidogun and C. W. Omlin, “A Self–Organizing Maps Model for Outlier Detection in Call Data from Mobile Telecommunication Networks”, In Proceedings of 8th Southern African Telecommunication Networks & Applications Conference (SATNAC 2004), September 2004.
[34]P. Berkhin, “Survey of clustering data mining techniques”, Technical Report, Accrue Software, 2002.
[35]P. Kotler and G. Armstrong, Principles of Marketing, 8th edition, New Jersey: Prentice Hall, 1999.
[36]Qining Lin, "Mobile Customer Clustering Analysis Based on Call Detail Records", Communications of the IIMA, Vol. 7, Issue 4, 2007.
[37]R.S. Wendell, “Product Differentiation and Market Segmentation as Alternative Marketing Strategies,” Journal of Marketing, Vol.21, pp.3-8, 1956.
[38]R.P. Lippmann, “An Introduction to Computing with Neural Nets”, IEEE ASSP Mag., pp. 4-22, 1987.
[39]Ross Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufmann Publishers, San Mateo, CA, 1993.
[40]S. Grandville, R. Peter J., Finding Groups in Data: An Introduction to Cluster Analysis, Lavoisier, 2005.
[41]T.R. Golub, et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring”, Science, 286(5439): pp. 531-7, 1999.
[42]T. Kohonen, “Self-organized formation of topologically correct feature maps”, Biological Cybernetics, Vol. 43, pp.59-69, 1982.
[43]T . Kohonen, “The Self-Organizing Map, ” Proc. IEEE, Vol. 78, No. 9, pp.1464-1480, 1990.
[44]T. Kohonen & H.Ritter, “Self-organizing semantic maps”, Biological Cybernetics, 61, pp.241-254, 1989.
[45]T. Kohonen, J. Hynninen, J. Kangas, J. Laaksonen, “SOM_PAK: The Self-Organizing Map Program Package”, Ver. 3.1, April 1995.
[46]W.-G. Teng and M.-C. Chou, “Mining Communities of Acquainted Mobile Users on Call Detail Records”, Proceedings of the 22nd Annual ACM Symposium on Applied Computing, pp.957-958, March 2007.
[47]W. Liu & Y, Luo., "Applications of clustering data mining in customer analysis in department store", In Proc. IEEE Int. Conf. Services Systems and Services Management, Vol.2, pp.1042-1046, 2005.
[48]W.W. Cohen, “Learning Rules that Classify E-mail”, In Proceedings of Machine Learning in Information Access: AAAI, Spring Symposium (SS-96-05), pp. 18-25, 1996.
[49]Wei, C. P. and Chiu, I. T. "Turning telecommunication call details to churn prediction: a data mining approach”, Expert Systems with Application, 23(2), pp.103-112.
[50]Wen-Jet Hsieh , ”The Analysis and Application of Grey Model and Back-Propagation Network to the Premium Rate Service”, Tatung University, Thesis for Master of Science, Department of Computer Science and Engineering, 2003.
指導教授 蔡孟峰 審核日期 2010-8-15
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