臺灣博碩士論文加值系統

俗語說三分天註定，七分靠打拼，古老的智慧勉勵人們，只要肯努力都會有成功的一日，在埋頭苦幹的同時，社會中的許多例子：如政治世家、豪門後代的存在，卻像是這句古諺最好的反例，究竟成功能不能只靠打拼？先天的背景到底對後天有多大的影響？想對成功的因素有更多的認知，就不能不了解這些問題的答案。
歷年來有許多這方面的文獻研究，在經濟學跟社會學的理論架構下，經濟不平等的世代傳承是被認為存在的，換句話說，父母親的社經地位對於子代的社會成就是有顯著的關聯性，除了直接相關以外，兩者間的過渡因素也是熱門的討論題材，其中最廣為探討的因素就是教育成就。教育自古一向是華人社會的重要價值，根據教育部的統計報告指出，2015年教育支出佔台灣政府歲出五分之一，近六年佔GDP 5.2% ~ 6% 之間，高於德國、日本等先進國家，而對於教育政策修訂也是社會上十分關心的議題，傳統上認為教育在促進階級流動上有著絕對性的影響 --- 所謂「十年寒窗無人問，一舉成名天下知」，教育與社會階級的關係也一直是熱門的研究議題。
然而受限於資料收集等等研究限制，多數的相關研究只能侷限於水平時間上，因此大多數的研究皆是以學生在校活動及其家庭背景為變因，探討對於學生學習表現的影響。而在社會階級流動或是對於個人成就之變因探討方面，多數研究從社會結構分類切入，或是由父母親的教育程度、家庭收入及人口統計因素為背景，變數不能完整反應學生本人、家庭背景及學習的狀態。本文研究資料取自臺灣教育長期追蹤資料庫（Taiwan Educational Panel Survey），該計畫在 2001 年至 2007 年，每兩年進行一次、總共四波的資料蒐集，調查面向包含了學生問卷、家長問卷及老師、學校問卷，作為變因能夠完整反映學生當時的背景，同時並串接台灣教育長期追蹤資料庫後續調查（TEPS-B），以學生初入社會之工作收入狀況作為目標，探討變因對其之影響。
本研究在資料清理後，先以隨機森林演算法的分類準確率來篩選重要變因，接著對各資料庫算出非相似度矩陣，用網格搜索算出最佳權重後，以合成的非相似度矩陣進行分群，分群採用Genetic K Means演算法，確保其為全域最佳解，接者再對每一分群各自進行關聯法則探勘，同時利用k-fold cross validation交互驗證，在修剪冗餘的法則後，找出滿足相關指標的重要法則。實驗結果可以從巨觀及微觀的角度呈現，巨觀上就所有子因素抽象化為自身價值觀、家庭背景及學校生活三方面來探討，發現在學生為高二階段，自身價值觀對未來收入的影響最高，家庭背景最低。微觀上來看，將所有子因素同時考慮並對應到六種不同的情節下，以易於理解的關聯法則形式來表現。

Education is widely regarded and serving as the primary pathway to allocate the economic remuneration. Basically, every study has implied the significant relationship between education achievement and earnings. Holding this belief, most people consider education as a mechanism that can sabotage the association of economic inequality transmitted from one generation to the next.
Examining the inheritance of economic privilege can be considered as a branch of issues that aims to find the determinants of one’s life achievement. Achievement typically denotes as socioeconomic status (SES) which is a combined measure based on one’s education, income and occupation in economic and sociological perspectives. The major theoretical view in economic literatures exploit the family behaviors by claiming families would transmit cultural and genetic characteristics to their offspring that effects children’s SES. Accordingly, a large number of empirical studies have discussed about relationship between family background and one’s SES.
In this study, we intend to propose a mechanism to investigate the determinants that take influence on one’s economic rewards. In order to bypass the limitation mentioned, we leverage diverse data mining techniques, instead of using statistical methods solely. By utilizing association rule mining, the relationships can be easily interpreted with if-then rules. Drawing on data from Taiwan Education Panel Survey (TEPS), a longitudinal dataset held in Taiwan, one’s background in the study stage can be represented completely in aspects of family, education and self-worth which are verified as determinants of future status.
Our main contribution is that we found the determinants of one’s earning would vary depending on macro or micro perspectives which are rarely mentioned in other works. In macro view, the relationship was discussed between earning and overall background of family, school and self-worth respectively. It showed that the self-worth of a student played the most important role, followed by school practice and family background. In micro view, the sub-factors were investigated in six scenarios respectively. While some of rules are supportive to the previous work as well as some are trivial, there are still unexpected and surprising insights found by our approach. These findings on one hand reflect the status-quo of our society, and on the other hand can be references in many aspects that guide us how to react for aiming a high-earning future.

口試委員會審定書 #
誌謝 i
中文摘要 ii
ABSTRACT iv
CONTENTS vi
LIST OF FIGURES x
LIST OF TABLES xi
Chapter 1 Introduction 1
1.1 Motivation & Objective 1
1.2 Contribution 5
1.3 Chapter outlines 5
Chapter 2 Literature Survey 6
2.1 Family background versus economic status 6
2.2 Education attainment versus economic status 7
2.3 Family background versus education attainment 8
2.4 Other factors versus economic status 8
2.5 Education expansion 9
Chapter 3 Background 11
3.1 Decision Tree 11
3.1.1 Classification and Regression Tree (CART) 13
3.2 Random Forest 16
3.2.1 Random Forest Similarity Measure 20
3.2.2 Variable Importance in Random Forest 21
3.3 Feature Selection by Boruta Algorithm 22
3.4 Clustering 24
3.4.1 Similarity Analysis 26
3.4.2 K-means Algorithm 28
3.4.3 Genetic Algorithm 30
3.4.4 Genetic K-means Algorithm 31
3.5 Determine Number of Clusters 33
3.5.1 Elbow Method 34
3.5.2 Gap Statistic 35
3.5.3 Prediction Strength 35
3.6 Association Rule Mining 37
3.7 Rule Pruning 41
3.7.1 Chi Square Test Pruning 42
3.7.2 Redundant Rule Pruning 44
3.8 Rule Ranking 45
3.8.1 Support, Confidence and Cardinality Ranking 45
3.8.2 Chi-squared Measure Ranking 46
Chapter 4 System Architecture 48
4.1 System Overview 48
4.2 Clustering Stage 49
4.3 Rule Generation Stage 50
4.4 Rule Extraction Stage 51
Chapter 5 Taiwan Education Panel Survey 55
5.1 Taiwan Education Panel Survey 55
5.2 Data Preprocessing 58
5.3 Clustering Stage 60
5.4 Rule Generation Stage 70
5.5 Rule Extraction Stage 73
Chapter 6 Results and Discussions 76
6.1 LowHozSel scenario 80
6.2 HiHozSel scenario 81
6.3 LowVerSel scenario 82
6.4 HiVerSel scenario 83
6.5 LowVerAll scenario 84
6.6 HiVerAll scenario 85
Chapter 7 Conclusions 86
Bibliography 88

[1] Becker, G. S. (1988). Family economics and macro behaviors. The American Economic Review, p.10
[2] Bowles, S., & Gintis, H. (2002). The inheritance of inequality. The Journal of Economic Perspectives, 16, 3–30.
[3] Bowles, S., Gintis, H., & Osborne Groves, M. (2005). Introduction. In S. Bowles, H. Gintis, & M. Osborne Groves (Eds.), Unequal chances Family background and economic success. New York: Russell Sage, pp. 1–22.
[4] R. Rumberger (2010), Education and the reproduction of social inequality in the United States: An empirical investigation, Economics of Education Review, 29 (2), pp. 246–254
[5] Becker, S. G., & Tomes, N. (1986). Human capital and the rise and fall of families. Journal of Labor Economics, 4, S1–S39.
[6] Bowles, S., Gintis, H., & Osborne, M. (2001). The determinants of earnings: A behavioral approach. Journal of Economic Literature, 39, 1137–1176.
[7] Timm, N.H., (2002). Applied Multivariate Analysis, Springer-Verlag, New York.
[8] Izenman, A.J., (2008). Modern Multivariate Statistical Techniques-Regression, Classification, and Manifold Learning. Springer, New York.
[9] Chang, Ly-yun. (2003). Taiwan Education Panel Survey: Users’ Guide and
The First Wave (2001). Center for Survey Research, Academia Sinica.
[10] Ping-yin Kuan. (2014). Taiwan Education Panel Survey and Beyond/2009: Telephone Follow-Up Survey of Panel-1 SH. Available from Survey Research Data Archive, Center for Survey Research, Research Center for Humanities and Social Sciences, Academia Sinica Web site: https:// srda.sinica.edu.tw
[11] Haveman, R., & Wolfe, B. (1995). The determinants of children’s attainments: A review of methods and findings. Journal of Economic Literature, 33, 1829–1878.
[12] Mazumder, B. (2005a). The apple falls even closer to the tree than we thought: New and revised estimates of the intergenerational inheri- tance of earnings. In S. Bowles, H. Gintis, & M. Osborne Groves (Eds.), Unequal chances: Family background and economic success (pp. 80–99). New York: Russell Sage.
[13] Mazumder, B. (2005b). Fortunate sons: New estimates of intergenera- tional mobility in the United States using social security earnings data. Review of Economics and Statistics, 87, 235–255.
[14] Levine, D. I., & Mazumder, B. (2007). The growing importance of family: Evidence from brothers’ earnings. Industrial Relations, 46, 7–21.
[15] Jencks, C., Smith, M., Acland, H., Bane, M. J., Cohen, D., Gintis, H., et al. (1972). Inequality: A reassessment of the effects of family and schooling in America. New York: Basic Books.
[16] Farkas, G. (2003). Racial disparities and discrimination in education: What do we know, how do we know it, and what do we need to know? Teachers College Record, 105, 1119–1146.
[17] Kao, G., & Thompson, J. S. (2003). Racial and ethnic stratification in edu- cational achievement and attainment. Annual Review of Sociology, 29, 417–442.
[18] Coleman J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95-S120 .
[19] Coleman J. S. (1990). Foundations of Social Theory. Cambridge, MA: Belknap Press of Harvard University Press.
[20] Beller, A. & Chung, S. S. (1992). Family structure and educational attainment of children: Effect of remarriage. Journal of Population Economics, 5, 309-320.
[21] Ingels, S. J., Scott, L. A., Lindmark, J. T., Frankel, M. R., & Myers, S. L. (1992). National education longitudinal study of 1988, first follow-up student component data file user’s manual. Washington, D.C.: U.S. Department of Education.
[22] Beblo M. & Lauer, C. (2004). Do family resources matter? Economics of Transition, 12 (3), 537-558.
[23] Shavit Y., Yaish, M., & Bar-Haim, E. (2007). The Persistence of persistent inequality. In S. Scherer, R. Pollak, G. Otte, & Markus Gangl (eds.), From Origin to Destination: Trends and Mechanisms in Social Stratification Research (pp. 37-57). Frankfurt: Campus Verlag.
[24] Osborne Roves, M. (2005). Personality and the intergenerational transmis- sion of economic status. In S. Bowles, H. Gintis, & M. Osborne Groves (Eds.), Unequal chances: Family background and economic success (pp. 208–231). New York: Russell Sage.
[25] Heckman, J. J., & Rubinstein, Y. (2001). The importance of noncognitive skills: Lessons from the GED testing program. American Economic Review, 91, 145–149.
[26] Farkas, G. (2003). Cognitive skills and noncognitive traits and behaviors in stratification processes. Annual Review of Sociology, 29, 541–562.
[27] Eccles, J. S. (2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44, 78−89.
[28] Julie S. Ashby & Ingrid Schoon (2010), Career success: The role of teenage career aspirations, ambition value and gender in predicting adult social status and earnings, Journal of Vocational Behavior 77 (2010) 350–360
[29] Cameron, S. V., & Heckman, J. J. (2001). The dynamics of educational attain- ment for Black, Hispanic, and White males. Journal of Political Economy, 109, 455–499.
[30] Mare, R. D. (1980). Social background and school continuation decisions. Journal of the American Statistical Association, 75, 295-305.
[31] Mare, R. D. (1981). Change and stability in educational stratification. American Sociological Review, 46, 72-87.
[32] Stolzenberg, R. M. (1994). Educational continuation by college graduates. American Journal of Sociology, 99, 1042-1077.
[33] Mullen, A. L., Goyette, K. A., & Soares, J. A. (2003). Who goes to graduate School? Social and academic correlates of educational continuation after college. Sociology of Education, 76(2), 143-169.
[34] Hout, M. (2007). Maximally Maintained Inequality Revisited: Irish Educational Mobility in Comparative Perspective. In M. N. Phadraig and E. Hilliard (eds.), Changing Ireland in International Comparison (pp. 23-40). Dublin: Liffey Press.
[35] Torche, F. (2011). Is a College Degree Still the Great Equalizer? Intergenerational Mobility across Levels of Schooling in the United States1.American Journal of Sociology, 117(3), 763-807.
[36] T. R. Hancock, T. Jiang, M. Li, and J. Tromp (1996). Lower bounds on learning decision lists and trees. Inform. Comput., vol. 126, no. 2, pp. 114–122.
[37] H. Zantema and H. L. Bodlaender (2000). Finding small equivalent decision trees is hard. Int. J. Found. Comput. Sci., vol. 11, no. 2, pp. 343–354.
[38] J. R. Quinlan, “Induction of decision trees (1986). Mach. Learn., vol. 1, pp. 81–106.
[39] L. Breiman, J. Friedman, R. Olshen, and C. Stone (1984). Classification and Regression Trees. Belmont, CA: Wadsworth.
[40] Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.
[41] Breiman L (2001). “Random Forests.” Machine Learning, 45, 5–32.
[42] Diaz-Uriarte, R, Alvarez de Andr´es, S (2006). Gene selection and classification of microarray data using random forest, BMC Bioinformatics, 7:3.
[43] G. Nimrod, A. Szilagyi, C. Leslie, N. Ben-Tal (2009). Identification of DNA-binding proteins using structural, electrostatic and evolutionary features, Journal of Molecular Biology 387 (4) 1040–1053.
[44] J. Ramirez, J.M. Gorriz, R. Chaves, M. Lopez, D. Salas-Gonzalez, I. Alvarez, F. Segovia (2009). SPECT image classification using random forests, Electronics Letters 45 (12) 604–605.
[45] A.G. Heidema, J.M.A. Boer, N. Nagelkerke, E.C.M. Mariman, D.L. van der A, E.J.M. Feskens (2006). The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases, Accident Analysis and Prevention 7 (23) 1–15. MD, USA2004, pp. 337–345.
[46] P. Han, X. Zhang, R.S. Norton, Z.P. Feng (2009). Large-scale prediction of long disordered regions in proteins using random forests, BMC Bioinformatics 10 (8) 1–9.
[47] P.A. Hernandez, I. Franke, S.K. Herzog, V. Pacheco, L. Paniagua, H.L. Quintana, A. Soto, J.J. Swenson, C. Tovar, T.H. Valqui, J. Vargas, B.E. Young (2008). Predicting species distributions in poorly-studied landscapes, Biodiversity and Conservation 17 1353–1366.
[48] M. Thums, C.J.A. Bradshaw, M.A. Hindell (2008). A validated approach for supervised dive classification in diving vertebrates, Journal of Experimental Marine Biology and Ecology 363 75–83.
[49] J. Peters, B. De Baets, N.E.C. Verhoest, R. Samson, S. Degroeve, P. De Becker, W. Huybrechts (2007). Random forests as a tool for ecohydrological distribution modelling, Ecological Modelling 207 304–318.
[50] J. Peters, N.E.C. Verhoest, R. Samson, M. Van Meirvenne, L. Cockx, B. De Baets (2009). Uncertainty propagation in vegetation distribution models based on ensemble classifiers, Ecological Modelling 220 791–804.
[51] S. Bernard, L. Heutte, S. Adam (2007). Using random forests for handwritten digit recognition, ICDAR: Ninth International Conference on Document Analysis and Recognition, vol. 1–2, Curitiba, Brazil 2007, pp. 1043–1047
[52] H.T. Chen, T.L. Liu, C.S. Fuh (2006). Segmenting highly articulated video objects with weak-prior random forests, in: A. Leonardis, H. Bischof, A. Pinz (Eds.), ECCV 2006, Part IV, Lecture Notes in Computer Science, vol. 3954, Springer- Verlag, Berlin, Heidelberg, pp. 373–385.
[53] J. Ham, Y. Chen, M.M. Crawford, J. Ghosh (2005). Investigation of the random forest framework for classification of hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing 43 (3) 492–501.
[54] M.M. Crawford, J. Ham, Y. Chen, J. Ghosh (2003), Random forests of binary hierarchical classifiers for analysis of hyperspectral data, in: IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, IEEE, Greenbelt,
[55] S.R. Joelsson, J.A. Benediktsson, J.R. Sveinsson (2006), Feature selection for morphological feature extraction using random forests, in: Seventh Nordic Signal Processing Symposium, IEEE, New York, Reykjavik, Iceland, pp. 138–141.
[56] I. Oparin, O. Glembek, L. Burget, J. Cernocky (2008). Morphological random forests for language modeling of inflectional languages, in: IEEE Workshop on Spoken Language Technology, SLT 2008, IEEE, Goa, India, pp. 189–192.
[57] Hastie, T., Tibshirani, R., Friedman, J., (2001). The Elements of Statistical Learn-ing, Springer, Berlin; Heidelberg; New York.
[58] L. Breiman (2004), RFtools—for predicting and understanding data, Technical Report, Berkeley University, Berkeley, USA /http://oz.berkeley.edu/users/breiman/RandomForests/cc.papers.htm.
[59] Aslam, Javed A.; Popa, Raluca A.; and Rivest, Ronald L. (2007); On Estimating the Size and Confidence of a Statistical Audit, Proceedings of the Electronic Voting Technology Workshop (EVT ''07), Boston, MA, August 6, 2007
[60] Liaw, Andy (2012). "Documentation for R package randomForest". Retrieved 15 March 2013.
[61] Verikas, A., Gelzinis, A., & Bacauskiene, M. (2011). Mining data with random forests: A survey and results of new tests. Pattern Recognition, 44(2), 330-349.
[62] Biau, G., Devroye, L., Lugosi, G., (2008). Consistency of random forests and other averaging classifiers. Journal of Machine Learning Research. 9, 2039- 2057.
[63] Torgerson, W.S., (1952). Multidimensional scaling: I. Theory and method. Psychometrika 17, 401–419.
[64] Hastie, T., Tibshirani, R., Friedman, J., (2011). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York . (corrected 5th printing of 2nd edition).
[65] Rudnicki WR, Kierczak M, Koronacki J, Komorowski J (2006). “A Statistical Method for Determining Importance of Variables in an Information System.” In S Greco, H Y, S Hirano, M Inuiguchi, S Miyamoto, HS Nguyen, R Slowinski (eds.), Rough Sets and Current Trends in Computing, 5th International Conference, RSCTC 2006, Kobe, Japan, November 6– 8, 2006, Proceedings, volume 4259 of Lecture Notes in Computer Science, pp. 557–566. Springer-Verlag, New York.
[66] Kohavi R, John GH (1997). “Wrappers for Feature Subset Selection.” Artificial Intelligence, 97, 273–324.
[67] Nilsson R, Pen ̃a J, Bjo ̈rkegren J, Tegn ́er J (2007). “Consistent Feature Selection for Pattern Recognition in Polynomial Time.” The Journal of Machine Learning Research, 8, 612.
[68] Kursa, M. B., Jankowski, A., & Rudnicki, W. R. (2010). Boruta A System for Feature Selection. Fundamenta Informaticae, 101(4), 271-285.
[69] MacQueen, J. B. (1967). Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. pp. 281–297. MR 0214227. Zbl 0214.46201. Retrieved 2009-04-07.
[70] Ester, Martin; Kriegel, Hans-Peter; Sander, Jörg; Xu, Xiaowei (1996). Simoudis, Evangelos; Han, Jiawei; Fayyad, Usama M., eds. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press. pp. 226–231.
[71] Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander (1999). OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD international conference on Management of data. ACM Press. pp. 49–60.
[72] D. R. Jones and M. A. Beltramo (1991), “Solving partitioning problems with genetic algorithms,” in Proc. 4th Int. Conf. Genetic Algorithms. San Mateo, CA: Morgan Kaufman.
[73] G. P. Babu and M. N. Murty (1994), “Simulated annealing for selecting initial seeds in the k-means algorithm,” Ind. J. Pure Appl. Math., vol. 25, pp. 85–94.
[74] G. P. Babu (1994), “Connectionist and evolutionary approaches for pattern clustering,” Ph.D. dissertation, Dept. Comput. Sci. Automat., Indian Inst. Sci., Bangalore, Apr.
[75] Krishna K, Murty M (1999). “Genetic K-means algorithm”, IEEE Transactions on Systems, Man and Cybernetics ,Part B: Cybernetics, 29:433-439.
[76] J. H. Holland (1975), Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. of Michigan Press.
[77] Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.
[78] Tibshirani, R., & Walther, G. (2005). Cluster validation by prediction strength. Journal of Computational and Graphical Statistics, 14(3), 511-528.
[79] David J. Ketchen, Jr & Christopher L. Shook (1996). "The application of cluster analysis in Strategic Management Research: An analysis and critique". Strategic Management Journal 17 (6): 441–458.
[80] Robert L. Thorndike (1953). "Who Belongs in the Family?". Psychometrika 18 (4): 267–276.
[81] R. Duda, P. Hart (1973), Pattern Classification and Scene Analysis, John Wiley and Sons, New York.
[82] A.K. Jain, R.C. Dubes (1988), Algorithms for Clustering Data, Prentice Hall, Englewood Cliff, New Jersey.
[83] Agrawal, R., Imielinski, T., and Swami, A. N. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207-216.
[84] Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, 487-499.
[85] Topor, R. & Shen, H. (2001) Construct robust rule sets for classification. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada: ACM Press, pp. 564–569.
[86] Antonie, M.-L., Za ̈ıane, O. R. (2003), Coman, A. Associative Classifiers for Medical Images, In Mining Multimedia and Complex Data (LNAI 2797), Springer-Verlag, 68– 83
[87] Thabtah, F. (2007). A review of associative classification mining. The Knowledge Engineering Review, 22(01), 37-65.
[88] Snedecor, W. & Cochran, W. (1989). Statistical Methods, 8th edn. Iowa City, IA: Iowa State University Press.
[89] Li, W., Han, J. & Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple-class association rule. In Proceedings of the International Conference on Data Mining (ICDM’01), San Jose, CA, pp. 369–376.
[90] Baralis, E., Chiusano, S. & Graza, P. (2004). On support thresholds in associative classification. In Proceedings of the 2004 ACM Symposium on Applied Computing. Nicosia, Cyprus: ACM Press, pp. 553–558.
[91] Thabtah, F., Cowling, P. & Peng, Y. (2004). MMAC: A new multi-class, multi-label associative classification approach. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04), Brighton, UK, pp. 217–224.
[92] Thabtah, F., Cowling, P. & Peng, Y. (2005). MCAR: Multi-class classification based on association rule approach. In Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications, Cairo, Egypt, pp. 1–7.
[93] Liu, B., Hsu, W. & Ma, Y. (1998). Integrating classification and association rule mining. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. New York, NY: AAAI Press, pp. 80–86.
[94] Li, W., Han, J. & Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple-class association rule. In Proceedings of the International Conference on Data Mining (ICDM’01), San Jose, CA, pp. 369–376.
[95] Antonie, M. & Zaïane, O. (2004). An associative classifier based on positive and negative rules. In Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. Paris, France: ACM Press, pp. 64–69.
[96] Ye, Y., Li, T., Jiang, Q., & Wang, Y. (2010). CIMDS: adapting postprocessing techniques of associative classification for malware detection. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(3), 298-307.
[97] Ly-yun Chang. (2003). Taiwan Education Panel Survey: The First Wave (Student Data) (C00124_A) [Data file]. Available from Survey Research Data Archive, Academia Sinica Web site: http:// srda.sinica.edu.tw
[98] Ly-yun Chang. (2003). Taiwan Education Panel Survey: The First Wave (Teacher Data) (C00124_C) [Data file]. Available from Survey Research Data Archive, Academia Sinica Web site: http:// srda.sinica.edu.tw
[99] Ly-yun Chang. (2003). Taiwan Education Panel Survey: The First Wave (Parent Data) (C00124_G) [Data file]. Available from Survey Research Data Archive, Academia Sinica Web site: http:// srda.sinica.edu.tw