1. 江文雄、田振榮,民86,高職實用技能班實施成效與學習策略之研究。臺北:教育部技職司。
2. 行政院主計處,民90,國民中小學中輟學原因之統計資料分析,台北。
3. 吳芝儀,民90,中輟學生的危機與轉機,台北,濤石出版社。
4. 吳美枝,民90,中輟學生問題與輔導之行動研究,國立中正大學犯罪防治研究所碩士論文。5. 法務部,民91,少年犯罪概況及其分析,台北:法務部犯罪研究中心。
6. 邱義堂,民89,通信資料庫之資料挖掘:客戶流失預測之研究,國立中山大學資訊管理學系研究所碩士論文。7. 段秀玲,民77,中途輟學國中生與一般國中生在生活適應及親子關係上差異之比較研究,輔導月刊,24卷,頁31-41。8. 商嘉昌,民83,中途輟學及青少年犯罪:以新竹少年監獄為例,國立政治大學社會學研究所碩士論文。9. 張人傑,民83,改進輟學研究需解決的問題,教育研究雙月刊,37期,頁28-35。10. 張佃富、邱文忠,民83,高級職業學校學生中途輟學原因與輔導策略之研究,教育廳專案研究報告,台灣省教育廳。
11. 教育部,民82,「延長以職業教育為主的國民教育」第一、二、三階段計畫執行概況及成效檢討報告。
12. 教育部,民84,職業學校法修正條文,延教班更名為實用技能班,教育部公報,頁7-8。
13. 教育部中部辦公室,民90,台灣區九十學年度辦理實用技能班概況手冊,臺中。
14. 梁志成,民82,台北市高及職業學校學生中途輟學因素及其輔導預防策略調查研究,國立台灣師範大學工業教育研究所碩士論文。15. 許文敏,民90,實用技能班學生學習滿意度之研究,國立台灣師範大學工業教育研究所碩士論文。16. 黃木添、王明仁,民87,兒童虐待的原因及預防,社區發展季刊,81期,頁189-196。17. 葉涼川譯,民90,CRM Data Mining 應用系統建置,麥格羅.希爾國際出版公司。
18. 劉玉玲,民91,青少年心理學,台北,揚智文化事業。
19. 鄭重趁,民88,中途學校與中輟生輔導,訓育研究,38卷2期,頁45-48。
20. 鄭增財,民89,實用技能班學生價值觀與學習行為之分析研究,國立台灣師範大學工業教育研究所博士論文。21. 鄧煌發,民90,國中生輟學原因及其偏差行為相關性之研究,國立中央警察大學犯罪防治研究所博士論文。22. 羅清水,民89,教育政策執行評估之研究─ 以高職實用技能班政策為例,國立臺灣師範大學博士論文。23. Battin-Pearson, S., Newcomb, M.D., Abbott, R.D., Hill, K.G., Catalano, R.F., & Hawkins, J.D. (2000), “Predictors of early high school dropout: A test of five theories,” Journal of Educational Psychology, 91, 568-582.
24. Berry J. A. and Linoff G. (1997), Data Mining Techniques : For Marketing, Sales, and Customer Support, John Wiley & Sons.
25. Bradley, Andrew P. and Lovell, Brian C. (1995), “Cost-Sensitive Decision Tree Pruning: Use of the ROC Curve ,” In Proceedings Eighth Australian Joint Conference on Artificial Intelligence, November, 1-8 / 565, Canberra, Australia.
26. Breiman,L., Friedman,J.H., Olshen,R.A., and Stone,C.J. (1984), “Classification and regression trees,” Chapman and Hall.
27. C. J. van RIJSBERGEN. (2003), “Information Retrieval,” http://www.dcs.gla.ac.uk/Keith.
28. Campbell, T. C., & Duffy, M. (1998). “Dropping out of secondary school: A descriptive discriminant analysis of early dropouts, late dropouts, alternative completers, and stayins.” Research in the Schools, 5(1), 1-10.
29. Chan P., Fan W., Prodromidis A., and Stolfo S. (1999), “Distributed data mining in credit card fraud detection,” IEEE Intelligent Systems, 146,67-74.
30. Duchenfield, M. (1997), “The performance of at-risk youth as tutors,” Technical report, SC: Clemsom University.
31. Ekstrom, R. B., Goertz, J. M., Pollack, J. M. & Rock, D. A. (1986), “Who Drops Out of High School and Why? Findings From a National Study,” In School Dropouts: Patterns and Policies , 52-69.
32. Elank C. (2002), “The Foundations of Cost-Sensitive Learning,” Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence.
33. Grupe, FH, and Owrang, MM, (1995), “Data Base Mining Discovering New Knowledge And Cooperative Advantage,” Information Systems Management, 12(4),26-31.
34. Hamilton, S.F. (1986), “Raising standard and reducing dropout rates,” Teacher College Record, 87, 410-429.
35. Han, J. & Kamber, M. (2001), “Data mining: concepts and techniques”., SanFrancisco: Morgan Kaufmann Publishers.
36. Janosz, M., Leblanc, M., Boulerice, B., and Tremblay, R.E. (1997), “Disentangling the weight of school dropout predictors: A test in two longitudinal samples,” Journal of Youth & Adolescence, 26, 733-762.
37. Janosz, M., Leblanc, M., Boulerice, B., and Tremblay, R.E. (2000), “Predicting Different Types of School Dropout: A Typological Approach with two longitudinal samples,” Journal of Educational Psychology, 92(1), 171-190.
38. Kass G.V. (1980), “An Exploratory Technique for Investigating Larg Quantities of Categorical Data,” Applied Sratistics, 29, 119-127.
39. Kretschmann, E. and Apweiler, R. (2001), “Automaticrule generation for protein annotation with the C4.5 data-mining algorithm applied on peptides in Ensembl. Proc.” http://www.bioinfo.de/isb/gcb01/talks/kretschmann/index.html.
40. Oakland, T. (1992), “School dropout: Characteristics and prevention.,” Journalof Adolescent Research, 1, 201-208.
41. Piatetsky-Shapiro G. and Frawley W. (1991), “Knowledge Discovery in Databases,” AAAI Press.
42. Prodromidis A., Chan P., and Stolfo S. (2000), “Meta-learning in distributed data mining systems: Issues and approaches,” In Advances in Distributed and Parallel Knowledge Discovery, H. Kargupta and P. Chan editors, Chapter 3, AAAI/MIT Press.
43. Quinlan, J.R. (1979). “Induction over large data bases.” Technical Report HPP-79-14, Heuristic Programming Project, Stanford University.
44. Quinlan,J.R. (1986), “Induction of decision trees,” Machine Learning, 1, 81-106.
45. Quinlan,J.R. (1993), “C4.5:Programs for Machine Learning.” San Mateo,CA:Morgan Kaufmann.
46. Shannon,C.E. (1984), “A mathematical Theory of Communication,” The Bell System Technical Journal, 27, 379-423.
47. Weiss, G.M., Provost F. (2001), “The Effect of Class Distribution on Classifier Learning: An Empirical Study,” Technical Report ML-TR-44,Department of Computer Science, Rutgers University