由於商業環境不斷快速變遷，迫使企業必須持續求新求變來因應。企業要能夠生存、發展，進而爭取市場競爭的主導權，除了不斷提昇產品的市場價值，更重要的是必須能有效地掌握市場資訊、迅速因應各種來自內外在環境的變化與挑戰。而在資訊科技的推波助瀾下，現今企業所面對的是一個與以往截然不同的競爭環境，不僅企業競爭的強度與速度倍數於以往，激增的市場交易也使得各企業所需儲存與處理的資料量越來越龐大。在這種情況下，企業的焦點已從以往的資料蒐集與整理，轉變成如何有效的利用資料庫來進行資訊的獲取。換言之，企業如何因應外界的競爭，有效的利用資料探勘（data mining）的技術與觀念，在龐大的資料庫中尋找出有價值的隱藏事件，以反應市場或消費者的需求，已成為各企業急於解決的重要議題之一。
本研究嘗試提出兩種演化式類神經網路的分類技術，一為整合傳統的鑑別分析與類神經網路，另一個則是整合模糊鑑別分析與類神經網路來進行資料探勘中判別模式的建立。主要的研究目的乃針對使用類神經網路其學習、收斂速度較慢的缺點進行改善，期望經由傳統鑑別分析與模糊鑑別方法的額外輸入資訊，以提供類神經網路一個良好的空間搜尋起始原點，再透過類神經網路的學習、辨識能力，來發展一個更為快速、精確的判別模式。為驗證提出方法的可行性，本研究針對兩種不同資料進行判別模式的建構，其一為統計教科書中常用的鳶尾花資料；另一個則為台灣某大型銀行的信用卡客戶申請資料。根據研究結果顯示，在二個實證資料下，類神經網路與鑑別分析、模糊鑑別分析之判別績效是優劣互見。而本研究所提之演化式類神經網路分類技術，不管是在鳶尾花或是銀行信用卡的資料上，其判別結果均較單純使用類神經網路者為佳，且網路收斂速度也較快；再者，與鑑別分析及模糊鑑別分析相較，演化式模糊類神經網路分類技術在鳶尾花資料的判別結果上與模糊鑑別分析相同；但在銀行信用卡資料上，判別結果則較模糊鑑別分析為佳。

Data mining is the art of finding patterns in data and is a new approach based on a general recognition that there is undraped value in large databases and utilities data-driven extraction of information. However, it is still not easy to identify the complicate relationship in the huge data set. Moreover, in most case, the estimation of parameters or the classification results can not really describe the realization of business modeling. The artificial neural network is becoming a very popular alternative in prediction and classification task due to its associated memory characteristic and generalization capability. However, neural network has been criticized by its long training process in the application of classification problems. In order to solve the above-mentioned drawback, the proposed study trying to explore the performance of data classification by integrating the artificial neural networks technique with the linear discriminant analysis and fuzzy discriminant analysis approach respectively.
To demonstrate the inclusions of the classification results from the linear discriminant and fuzzy discriminant analysis would improve the classification accuracy of the designed neural networks, classification tasks are performed on two data sets, the often used Iris data and one practical bank credit card data. As the results reveal, the two proposed integrated approach provides a better initial solution and hence converges much faster than the conventional neural networks. Besides, in comparison with the traditional neural network approach, the classification accuracies increase for both cases in terms of the two proposed methodology. Moreover, the superiority of the proposed technique can be observed by comparing the classification results using only linear discriminant or fuzzy discrimintant analysis approaches.

目 錄
目錄………………………………………………………………Ⅰ
圖目錄……………………………………………………………Ⅲ
表目錄……………………………………………………………Ⅴ
第壹章 緒論……………………………………………………1
第貳章 文獻回顧………………………………………………5
2.1、資料探勘…………………………………………………5
2.2、鑑別分析…………………………………………………9
2.3、模糊鑑別分析……………………………………………10
2.4、類神經網路………………………………………………11
第參章 研究方法………………………………………………14
3.1、鑑別分析…………………………………………………14
3.2、模糊鑑別分析……………………………………………15
3.3、倒傳遞類神經網路………………………………………19
第肆章 資料分析………………………………………………23
4.1、演化式鑑別方式…………………………………………23
4.1.1、花卉資料…………………………………24
4.1.2、銀行資料……………………………………………32
4.2、演化式模糊方式……………………………40
4.2.1、花卉資料………………………41
4.2.2、銀行資料………………………49
第伍章 結論……………………………………………………58
參考文獻………………………………………………………61
圖 目 錄
圖1：知識發現之流程……………………………………… 6
圖2：資料探勘之研究架構…………………………………9
圖3：神經元之構造………………………………………12
圖4 倒傳遞類神經網路之構造……………………………19
圖5：{4-9-1}類神經網路模式(1)之訓練樣本RMSE趨勢圖
-花卉資料………………………………………………….27
圖6：{5-9-1}演化式鑑別方式之訓練樣本RMSE趨勢圖
-花卉資料………………………………………………….30
圖7：{6-12-1}類神經網路模式(1)之訓練樣本RMSE趨勢圖
-銀行資料…………………………………………………35
圖8：{7-16-1}演化式鑑別方式之訓練樣本RMSE趨勢圖
-銀行資料………………………………………………….38
圖9：{4-8-1}類神經網路模式(2)之訓練樣本RMSE趨勢圖
-花卉資料…………………………………………………..44
圖10：{5-11-1}演化式模糊方式之訓練樣本RMSE趨勢圖
-花卉資料………………………………………………….47
圖11：{4-11-1}類神經網路模式(2)之訓練樣本RMSE趨勢圖
-銀行資料………………………………………………….52
圖12：{5-15-1}演化式模糊方式之訓練樣本RMSE趨勢圖
-銀行資料………………………………………………….55
表 目 錄
表1：花卉資料之鑑別分析結果………………………………25
表2：類神經網路模式(1)不同參數組合之預測結果-花卉資料26
表3：類神經網路模式(1)之判別結果-花卉資料………………27
表4：演化式鑑別方式在不同參數組合之預測結果-花卉資料29
表5：演化式鑑別方式之判別結果-花卉資料….………………31
表6：花卉資料之三種模式判別結果比較表…………………… 31
表7：銀行資料之鑑別分析結果………………………………… 32
表8：類神經網路模式(1)不同參數組合之預測結果-銀行資料34
表9：類神經網路模式(1)之判別結果-銀行資料………………36
表10：演化式鑑別方式不同參數組合之預測結果-銀行資料… 37
表11：演化式鑑別方式之判別結果-銀行資料………………… 38
表12：銀行資料之三種模式判別結果比較表…………………39
表13：三種模式之第一類及第一類錯誤率比較表-銀行資料…40
表14：花卉資料之模糊鑑別分析結果…………………………42
表15：類神經網路模式(2)不同參數組合之預測結果-花卉資料43
表16：類神經網路模式(2)之判別結果-花卉資料……………44
表17：演化式模糊方式在不同參數組合之預測結果-花卉資料46
表18：演化式模糊方式判別結果-花卉資料..…………………48
表19：花卉資料之三種模式判別結果比較表…………………49
表20：銀行資料之模糊鑑別分析結果…………………………50
表21：類神經網路模式(2)不同參數組合之預測結果-銀行資料51
表22：類神經網路模式(2)之判別結果-銀行資料………………53
表23：演化式模糊方式不同參數組合之預測結果-銀行資料…54
表24：演化式模糊方式之判別結果-銀行資料…………………56
表25：銀行資料之三種模式判別結果比較表…………………57

參考文獻
1.Berry, M.J.A. and Linoff, G. “Data Mining Technique for Marketing, Sale, and Customer Support”, Wiley Computer, 1997.
2.Bowen, J.E., “Using Neural Nets to Predict Several Sequential and Subsequent Future Values from Time Series Data”, The First International Conference on Artificial Intelligence in Wall Street, 1991, pp.30-34.
3.Cabena, P., Hadjinaian, P.O., Stadler, DR.J., Verhees, J. and Zanasi, A., “Discovering Data Mining from Concept to Implementation”, Prentice Hall, 1998.
4.Chen, M.S., Han, J. and Yu, P.S., “Data Mining: An Overview from a Database Perspective”, IEEE Transactions on Knowledge and Data Engineering, Vol.8, 1996, pp.866-883.
5.Chen, Z.P., Jiang, J.H., Li, Y., Liang, Y.Z. and Yu, R.Q., “Fuzzy Linear Discriminant Analysis for Chemical Data Sets”, Chemomentrics and Intelligent Laboratory Systems, Vol. 45, 1999, pp.295-302.
6.Chung, H.M. and Gray, P., Guest Editors “Special Section: Data Mining”, Journal of Management Information Systems, Vol.16, 1999, pp. 11-16.
7.Chu, C.H. and Widjaja, D. “Neural Network System for Forecasting Method Selection”, Decision Support System, Vol.12, 1994, pp.13-24.
8.Cooper, D.R. and Emory, C.W. “Business Research Method”, Orlando: Dryden, 1995.
9.Craven, M.W. and Shavlik, J.W. “Using Neural Networks for Data Mining”, Future Generation Computer Systems, Vol. 13, 1997, pp. 221-229.
10.Curt, H. “ The Devil’s in The Detail: Techniques, Tools, and Applications for Database mining and Knowledge Discovery-Part 1”, Intelligent Software Strategies, Vol.6, 1995, pp. 1-15.
11.Cybenko, G. “Approximation by Superpositions of a Sigmoidal Function”, Mathematical Control Signal Systems, Vol. 2, 1989, pp. 303-314.
12.Davies, P.C. “Design Issues in Neural Network Development”, NEUROVEST Journal, 1994, pp. 21-25.
13.Desai, V.S., Crook, J.N., Overstreet, Jr. G.A. “A Comparison of Neural Networks and Linear Scoring Models in the Credit Union Environment”, European Journal of Operational Research, Vol. 95, 1996, pp. 24-37.
14.Dillon, W. R., Goldstein, M. “Multivariate Analysis Methods and Applications”, Wiley, New York, 1984.
15.Dutta, S. and Shekhar, S. “ Bond Rating: A non-conservation Application of Neural Network”, IEEE International Conference on Neural Networks, San Diego, CA, Vol.2, 1988, pp.443-450.
16.Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. “The KDD Process for Extracting Useful Knowledge from Volumes of Data”, Communications of the ACM, Vol.39, November 1996, pp. 27-34.
17.Fish, K.E., Barnes, J.H. and Aiken, M.W. “Artificial Neural Networks: A New Methodology for Industrial Market Segmentation”, Industrial Marketing Management, Vol.24, 1995, pp. 431-438.
18.Freeman, J.A. and Skapura, D.M. “Neural Networks Algorithms, Applications, and Programming Techniques”, Addison-Wesley Publishing Company, 1992 .
19.Fu, Y. “Data Mining Task, Technique and Applications”, IEEE Potentials, October/November 1997.
20.Grupe, F.H., Owrang, M.M. “Database Mining Discovering New Knowledge and Cooperative Advantage”, Information Systems Management, Vol.12, Fall 1995, pp. 26-31.
21.Gustrfson, D.E. and Kessel, W.C. “Fuzzy Clustering with a Fuzzy Covariance Matrix”, Proceedings of IEEE CDC., 1979, pp.761-766.
22.Han, J. “Data Mining”, in Urban, J., Dasgupta, P. （Editors）, Encyclopedia of Distributed Computing, Kluwer Academic Publishers, 1999, pp.1-7.
23.Hand, D.J. “Data Mining: Statistics and More? ”, The American Statistician; Alexandria, Vol.52, May 1998.
24.Hornik, K., Stinchcombe, M. and White, H., “Multilayer Feedforward Networks are Universal Approximations”, Neural Networks, Vol. 2, 1989, pp. 336-359.
25.Johnson, R.A. and Wichern, D.W., “ Applied Multivariate Statistical Analysis”, Fourth Edition by Prentice-Hall Inc., 1998.
26.Kim, J.C., Kim, D.H., Kim, J.J., Ye, J.S. and Lee, H.S. “Segmenting the Korean Housing Market Using Multiple Discriminant Analysis”, Construction Management and Economics, Vol.18, Jan 2000, pp. 45-54.
27.Kleclka, W.R. Discriminant Analysis SAGA Publications, Inc. California, 1980.
28.Lee, H., Jo, H. and Han, I. “ Bankruptcy Prediction Using Case-Based Reasoning, Neural Networks, and Discriminant Analysis”, Expert Systems With Applications, Vol.13, 1997, pp. 97-108.
29.Lee, G., Sung, T.K., Chang, N. “ Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction”, Journal of Management Information Systems, Vol.16, Summer 1999, pp. 63-85.
30.Lippmann, R.P. “A introduction to Computing with Neural Nets”, IEEE ASSP Magazine, April 1987, pp. 4-22.
31.Malhotra, M.K., Sharma, S., Nair, S.S. “Decision Making Using Multiple Models ”, European Journal of Operational Research, Vol.114, 1999, pp. 1-14.
32.Michael, A.B., “Data Mining Techniques for Marketing, Sales, and Customer Support”, Wiley Computer Publishing, 1997.
33.Okuda, T., Tanaka, H. and Asai, K. “Discriminant Problem with Fuzzy States and Fuzzy Information”, TIMS/Studies in the Management Sciences, Vol. 20, 1980, pp. 97-106.
34.Qnet 97 – Neural Network Modeling for Windows 95/98/NT, Vesta Services, Winnetka, IL, 1998.
35.Rumelhart, D.E., Hinton, D.E. and Williams, R.J. “Learning Internal Representations by Error Propagation in Parallel Distributed Processing”, MIT Press, Cambridge, MA, 1986, pp.318-362.
36.Salchenberger, L.M., Cinar, E.M., Lash, N.A. “Neural Networks: A New tool for Predicting Thrift Failures ”, Decision Sciences, Vol.23, 1992, pp. 899-916.
37.Sanchez, M.S., Sarabia, L.A. “Efficiency of Multi-layered Feed-forward Neural Networks on Classification in Relation to Linear Discriminant Analysys, Quadratic Discriminant Analysis and Regularized Discriminant Analysis”, Chemometrics and Intelligent Laboratory Systems, Vol.28, 1995, pp. 287-303.
Sharma, S. “Applied Multivariate Techniques”, Wiley, New York, 1996.
38.SPSS 1997 – Statistic Modeling for Windows 95/98/NT, SPSS Inc., 1998 .
39.Sung, T.K., Chang, N., Lee, G. “Dynamics of Modeling in Data Mining; Interpretive Approach to Bankruptcy Prediction”, Journal of Mmanagement Information Systems, Vol.16, Summer 1999, pp. 63-85.
40.Tang, Z., Almeida, C. and Fishwick, P.A. “Time Series Forecasting Using Neural Networks vs. Box-Jenkins Methodology”, Simulation, Vol.57, 1991, pp.303-310.
41.Trevino, L.J., Daniels, J.D. “FDI Theory and Foreign Direct Investment in the United States: a Comparison of Investors and Non-investors ”, International Business Review, Vol.4, 1995, pp. 177-194.
42.Vellido, A., Lisboa, P.J.G. and Vaughan, J. “Neural Networks in Business: A Survey of Applications （1992-1998）”, Expert Systems With Applications, Vol. 17, 1999, pp. 51-70.
Watanabe, H., Yakowenko, W.J., Kim, Y.M., Anbo, J. and Tobi, T., “ Application of Fuzzy Discriminant Analysis for Diagnosis of Valvular Heart Disease”, IEEE Transactions on Fuzzy Systems, Vol. 2, 1994, pp. 267- 276.
43.Zadeh, L.A., “Fuzzy Sets”, Information and Control, Vol. 8, 1965, pp.338-353.
44.Zhang, G., Patuwo, B.E. and Hu, M.Y. “Forecasting with Artificial Neural Networks: The State of the Art”, International Journal of Forecasting, Vol.14, 1998, pp.35-62.