1.S. B. Achelis, Technical Analysis from A to Z. McGraw-Hill, New York, 2000.![new window](/gs32/images/newin.png)
2.R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases”, ACM SIGMOD Record, 22(2), 207-216, 1993.
3.R. Agrawal, H. Mannila, R. Srikant, H. Tovionen and A.I. Verkamo, “Fast discovery of association rules”, Advances in knowledge discovery and data mining table of contents, 307-328, 1996.
4.R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases”, in Proceedings of the 20th International Conference on Very Large Data Bases, 1994.
5.K. Ali, S. Manganaris and R. Srikant, “Partial classification using association rules”, in Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, The AAAI Press, Newport Beach, California,115-118, 1997.
6.M. Antonie and O. Zaiane, “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, 64–69, 2004.
7.M. Antonie, O. Zaiane and A. Coman, “Associative classifiers for medical images”, Mining Multimedia and Complex Data (Lecture Notes in Artificial Intelligence, Vol. 2797), Berlin: Springer, 68–83, 2003.
8.J.F. Baldwin and T.P. Martin, “Learning uncertain logic Programs from examples”, in Proceedings of the 2nd International Workshop on Logic Programming and Soft Computing, Manchester, UK, 1998.
9.E. Baralis and P. Torino, “A lazy approach to pruning classification rules”, in Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM’02), Maebashi City, Japan, 35-42, 2002.
10.E. Baralis, S. Chiusano and P. Graza, “On support thresholds in associative classification”, in Proceedings of the 2004 ACM Symposium on Applied Computing, Nicosia, Cyprus: ACM Press, 553–558, 2004.
11.R.J. Bauer, Jr., Genetic algorithms and investment strategies. Wiley, New York, 1994.
12.A. Berson, S. Smith, and K. Thearling, Building data mining applications for CRM. McGraw-Hill New York, 2000.
13.P.K. Chan, W. Fan, A.L. Prodromidis and S.J. Stolfo, “Distributed data mining in credit card fraud detection”, Intelligent Systems and Their Applications, IEEE (IEEE Intelligent Systems), 14(6), 67-74, 1999.
14.Y.W. Chang Chien and Y.L. Chen, “A Phenotypic Genetic Algorithm for Inductive Logic Programming”, Expert Systems with Applications, Vol. 36, Issue 3, Part 2, 6935-6944, 2009.
15.G. Chen, H. Liu, L. Yu, Q. Wei and X. Zhang, “A new approach to classification based on association rule mining”, Decision Support System, Vol.42, 674-689, 2006.
16.M.S. Chen, J. Han, and P. S. Yu, “Data mining: an overview from a database perspective”, IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883, 1996.
17.S.F. Chen and Y. Liu, “The application of multi-level genetic algorithms in assembly planning”, Journal of Industrial Technology, 17(4), 1-9, 2001.
18.R.G. Cowell, S.L. Lauritizen, A.P. David and D.J. Spiegelhalter, Probabilistic networks and expert systems. Springer-Verlag, New York, 1999.
19.D. Dasgupta and D. R. McGregor, “sGA: a structured genetic algorithm”, technical report, University of Strathclyde, UK, 1992.
20.N. Dellaert, J. Jeunet and N. Jonard, “A genetic algorithm to solve the general multi-level lot-sizing problem with time-varying costs”, International Journal of Production Economics, 68(3), 241-257, 2000.
21.R. Durbin, S.R. Eddy, A. Krogh and G. Mitchison, Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, 1998.
22.W.J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus, “Knowledge discovery in databases: an overview”, AI Magazine, 13(3), 57-70, 1992.
23.P. Giudici, Applied data mining: statistical methods for business and industry. Wiley, 2003.
24.N. Guofang, L. Minqiang and K. Jisong, “Design and Analysis of Multi-level Genetic Algorithm with its Application to the Construction of Clock Binary Tree”, IJCSNS, 6(2), 75-82, 2006.
25.J. Han and M. Kamber, Data mining: concepts and techniques. 2nd edition, Morgan Kaufmann, 2006.
26.J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation”, ACM SIGMOD Record, 29(2), 1-12, 2000.
27.J. Hipp, U. Guntzer, and G. Nakhaeizadeh, “Algorithms for association rule mining: general survey and comparison”, ACM SIGKDD Explorations Newsletter, 2(1), 58-64, 2000.![new window](/gs32/images/newin.png)
28.J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge, 1992.
29.F.V. Jensen, Bayesian networks and decision graphs. Springer-Verlag, New York, 2001.
30.D.R. Jobman, The Handbook of Technical Analysis. Irwin, New York, 1995.
31.B. Kovalerchuk and E. Vityaev, “Inductive logic programming for discovering financial regularities”, in Proceedings of the Workshop on Data Mining in Finance (KDD 98), New York, 1-16, 1998.
32.B. Kovalerchuk and E. Vityaev, “Comparison of relational methods and attribute-based methods for data mining in intelligent systems”, in Proceedings of the 1999 IEEE international Symposium on Intelligent Control/ Intelligent Systems and Semiotics, 162-166, 1999.
33.B. Kovalerchuk and E. Vityaev, Data mining in finance: advanced in relational and hybrid methods. Kluwer Academic, Norwell, MA, 2000.
34.B. Kovalerchuk , E. Vityaev E and H. Yusupov, “Symbolic methodology in numeric data mining: relational techniques for financial applications”, ArXiv Computer Science e-prints, 1-20, 2002.
35.R. Kumar, K. Izui, Y. Masataka and S. Nishiwaki, “Multilevel Redundancy Allocation Optimization Using Hierarchical Genetic Algorithm”, IEEE Transactions on reliability, 57(4), 650-661, 2008.
36.R. Kumar, K. Izui, Y. Masataka and S. Nishiwaki, “Multilevel Redundancy Allocation Optimization Using Hierarchical Genetic Algorithm”, Reliability Engineering and System Safety, 94(4), 891-904, 2009.
37.N. Lavarc, S. Dzeroski and M. Grobelnik, “Learning non-recursive definitions of relations with LINUS”, in Proceedings of the 5th European Working Session on Learning, Springer-Verlag, 265-281, 1991.
38.W. Li, J. Han and J. Pei, “CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules”, in Proceedings of ICDM 2001, 369-376, 2001.
39.H.Y. Liu, J. Chen and G. Chen, “Mining insightful classification rules directly and efficiently”, in Proceedings of the 1999 IEEE International Conference on Systems Man and Cybernetics, IEEE Computer Society, Tokyo, 911-916, 1999.
40.B. Liu, W. Hsu and Y. Ma, “Integrating classification and association rule mining”, in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York City, USA, 80–86, 1998.
41.B. Liu, M. Hu and W. Hsu, “Multi-level organization and summarization of the discovered rules”, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000), Boston: ACM Press, 208-217, 2000.
42.B. Liu. Y. Ma and C.K. Wong, “Classification using association rules: weakness and enhancements”, in V. Kumar, et al., (Eds.), Data Mining for Scientific and Engineering Application, 591, 2001.
43.R. Mattison, Data warehousing and data mining for telecommunications. Artech House, Inc. Norwood, MA, USA, 1997.
44.C. Merz and P. Murphy, UCI repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science, 1996.
45.S. Muggleton, “Bayesian Inductive Logic Programming”, in Proceedings of the 7th Annual Conference on Computational Learning Theory, ACM, New York, 3-11, 1994.
46.S. Muggleton, “Inductive Logic Programming: derivations, successes and shortcomings”, SIGART Bulletin, 5(1), 5-11, 1994.![new window](/gs32/images/newin.png)
47.S. Muggleton and C. Feng, “Efficient induction of logic programs”, in Proceedings of the 1st Conference on Algorithmic Learning Theory, Ohmsma, Tokyo, Japan, 368-381, 1990.
48.J.J. Murphy, Intermarket technical analysis: trading strategies for the global stock, bond, commodity, and currency markets. Wiley, New York, 1991.
49.M. Pazzani, C. Brunk and G. Silverstein, “An information-based approach to integrating empirical and explanation-based learning”, in S. Muggleton(ed) Inductive Logic Programming, Academic Press, London, 373-394, 1992.
50.J. Pearl, Reasoning in intelligent systems: networks of plausible inference. Morgan Kaufman, PALO ALTO, CA, 1991.
51.G. Piatetsky-Shaprio, U. Fayyad and P. Smyth, “From data mining to knowledge discovery”, in G. Piatetsky-Shaprio, U. Fayyad, P. Smyth (Eds.), An Overview. Advanced in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1-35, 1996.
52.H. Prade and M. Serrurier, “Getting adaptability or expressivity in inductive logic programming by using fuzzy predicates”, in Proceedings of the IEEE International Conference on Fuzzy Systems, Budapest, Hungary, Vol. 1, 73-77, 2004.
53.J.R. Quinlan, “Induction of decision trees”, Machine Learning, 1(1), 81-106, 1986.![new window](/gs32/images/newin.png)
54.J.R. Quinlan, “Simplifying decision trees”, International Journal of Man-Machine Studies, 27(3), 221-234, 1987.
55.J.R. Quinlan, “Learning logical definitions from relations”, Machine Learning, 5(3), 239-266, 1990.
56.J. R. Quilan, C4. 5: programs for machine learning. Morgan Kaufmann, 1993.
57.L.R. Rabiner and B.H. Juang, “An introduction to hidden markov models”, IEEE ASSP Magazine, 3(1), 4-16, 1986.![new window](/gs32/images/newin.png)
58.L.R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition”, in Proceedings of the IEEE, 77(22), 257-286, 1989.
59.L. Raedt and K. Kersting, “Probabilistic logic learning”, ACM SIGKDD Explorations Newsletters, 5(1), 34-48, 2003.![new window](/gs32/images/newin.png)
60.N. Safaei, S.J. Sadjadi, and M. Babakhani, “An efficient genetic algorithm for determining the optimal price discrimination”, Applied Mathematics and Computation, Vol. 181, 1693-1702, 2006.
61.M. Serrurier, D. Dubois, H. Prade and T. Sudkamp, “Learning fuzzy rules with their implication operators”, Data & Knowledge Engineering, Vol. 60, 71-89, 2007.
62.E.Y. Shapiro, Algorithmic program debugging, MIT Press, 1983.
63.R. Sullivan, A. Timmermann, and H. White, The dangers of data-driven inference: the case of calendar effects in stock returns. LSE Financial Markets Group, 1998.
64.F. Thabath, “A review of associative classification mining”, Knowledge Engineering Review, 22(1), 37-65, 2007.![new window](/gs32/images/newin.png)
65.F. Thabtah, P. Cowling and Y. Peng, “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, 217–224, 2004.
66.F. Thabtah, P. Cowling and Y. Peng, “MCAR: Multi-class classification based on association rule approach”, in Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications, Cairo, Egypt, 1–7, 2005.
67.S. Thawornwong, D. Enke and C. Dagli, “Neural networks as a decision maker for stock trading: a technical analysis approach”, Journal of Smart Engineering Systems Design, Vol. 5, 1-13, 2003.
68.K. Wang, L. Tang, J. Han and J. Li, “Top down FP-Growth for association rule mining”, in Proceedings of 6th Pacific-Asia conference on Knowledge Discovery and Data Mining, 2002.
69.K. Wang, S. Zhou and Y. He, “Growing decision tree on support-less association rules”, in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, 265–269, 2000.
70.X. Xu, G. Han and H. Min, “A novel algorithm for associative classification of images blocks”, in Proceedings of the 4th IEEE International Conference on Computer and Information Technology, Lian, Shiguo, China, 46–51, 2004.
71.X. Yin and J. Han, “CPAR: Classification based on predictive association rules”, in Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, USA, 208–217, 2003.
72.O. Zaiane and A. Antonie, “Classifying text documents by associating terms with text categories”, in Proceedings of the 13th Australasian Database Conference (ADC’02), Melbourne, Australia, 215–222, 2002.
73.M. J. Zaki, “Generating non-redundant association rules”, in Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000.
74.M. J. Zaki, S. Parthasarathy, M. Ogihara and W. Li, “New algorithms for fast discovery of association rules”, in Proceedings of 3rd Intl. Conf. on Knowledge Discovery and Data Mining, 1997.