Discovering interesting knowledge from a science and technology database with a genetic algorithm

Applied Soft Computing Journal

Volumn 4, Issue 2, 2004, Pages 121-137

  Romão, Wesley ^a   Freitas, Alex A ^b   Gimenes, Itana M De S ^a  

^a STATE UNIVERSITY OF MARINGÁ   (Brazil)

^b UNIVERSITY OF KENT   (United Kingdom)

Author keywords

Classification; Data mining; Genetic algorithms; Rule interestingness

Indexed keywords

ARTICLE; COMPREHENSION; DATA BASE; EVALUATION; GENETIC ALGORITHM; LINGUISTICS; LITERATURE; PREDICTION; SCIENCE; TECHNIQUE; TECHNOLOGY; THEORETICAL MODEL;

EID: 2442597364     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2003.10.002     Document Type: Article

References (37)

1. T. Blickle, Tournament selection, in: T. Back, D.B. Fogel, Z. Michalewicz (Eds.), Evolutionary Computation 1: Basic Algorithms and Operators, Institute of Phisics Publishing, 2000 (Chapter 24).
2. C.C. Bojarczuk, H.S. Lopes, A.A. Freitas, Genetic programming for knowledge discovery in chest pain diagnosis, IEEE Engineering in Medicine and Biology Magazine, 19(4) - special issue on data mining and knowledge discovery, IEEE Computer Society, Los Alamitos, CA, USA, 2000, pp. 38-44.
3. K.A. Crockett, Z. Bandar, A. Al-Attar, Soft decision trees: a new approach using non-linear fuzzification, in: Proceedings of the Ninth IEEE International Conference on Fuzzy Systems, FUZZ IEEE'2000, IEEE Computer Society, Los Alamitos, CA, USA, 2000, pp. 209-215.
4. K. Deb, Multiobjective Optimization Using Evolutionary Algorithms, Wiley, New York, 2001.
5. V. Dhar, D. Chou, F. Provost, Discovering Interesting Patterns for Investment Decision Making with GLOWER - A Genetic Learner Overlaid With Entropy Reduction, Data Mining and Knowledge Discovery, vol. 4, no. 4, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000, pp. 251-280.
6. J. Espinosa, J. Vandewalle, Constructing fuzzy models with linguistic integrity from numerical data - AFRELI algorithm, IEEE Trans. on Fuzzy Systems, vol. 8, no. 5, IEEE Computer Society, Los Alamitos, CA, USA, 2000, pp. 591-600.
7. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, Advances in knowledge discovery and data mining, From data mining to knowledge discovery: an overview, AAAI/MIT Press, Menlo Park, CA, USA, 1996 (Chapter 1).
8. A.A. Freitas, On objective measures of rule surprisingness, Principles of Data Mining and Knowledge Discovery, in: Proceedings of the Second European Symposium on PKDD-98, Lecture Notes in Artificial Intelligence 1510, Springer, Berlin, 1998, pp. 1-9.
9. A.A. Freitas, Understanding the crucial differences between classification and discovery of association rules - a position paper, ACM SIGKDD Explorations, vol. 2, no. 1, ACM, New York, USA, 2000, pp. 65-69.
10. A.A. Freitas, Understanding the Crucial Role of Attribute Interaction in Data Mining, Artificial Intelligence Review, vol. 16, no. 3, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2001, pp. 177-199.
11. A.A. Freitas, Data Mining and Knowledge Discovery with Evolutionary Algorithms, Springer, Berlin, 2002.
12. D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, New York, 1989.
13. J. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, USA, 2000.
14. D.J. Hand, Construction and Assessment of Classification Rules, Wiley, New York, 1997.
15. R.J. Hilderman, H.J. Hamilton, Knowledge discovery and measures of interest, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2001.
16. K. Hirota, W. Pedrycz, Fuzzy computing for data mining, in: Proceedings of the IEEE Intelligent Systems, vol. 87, no. 9, Los Alamitos, CA, USA, 1999, pp. 1575-1600.
17. H. Ishibuchi, T. Nakashima, Designing Compact Fuzzy Rule-Based Systems with Default Hierarchies for Linguistic Approximation, CEC-99, Washington, DC, USA, 1999, pp. 2341-2348.
18. Y. Kim, W.N. Street, F. Menczer, Feature selection in unsupervised learning via evolutionary search, in: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD - 2000, ACM Press, USA, 2000, pp. 365-369.
19. B. Liu, W. Hsu, Post-Analysis of Learned Rules, AAAI-96, AAAI Press, Portland, Oregon, USA, 1996, pp. 828-834.
20. B. Liu, W. Hsu, S. Chen, Using general impressions to analyze discovered classification rules, in: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, KDD-97, AAAI Press, NewPort Beach, CA, USA, 1997, pp. 31-36.
21. B. Liu, W. Hsu, S. Chen, Y. Ma, Analyzing the subjective interestingness of association rules, in: Proceedings of the IEEE Intelligent Systems, vol. 15, no. 5, IEEE Computer Society, Los Alamitos, CA, USA, 2000, pp. 47-55.
22. J.A. Major, J.J. Mangano, Selecting among rules induced from a hurricane database, Knowledge Discovery in Databases Workshop at AAAI-93, AAAI Press, Washington, DC, USA, 1993, pp. 28-44.
23. R.R.F. Mendes, F.B. Voznika, A.A. Freitas, J.C. Nievola, Discovering fuzzy classification rules with genetic programming and co-evolution, Principles of Data Mining and Knowledge Discovery, in: Proceedings of the fifth European Conference on PKDD 2001 - Lecture Notes in Artificial Intelligence 2168, Springer, Berlin, 2001, pp. 314-325.
24. C. Mota, H. Ferreira, A. Rosa, Independent and Simultaneous Evolution of Fuzzy Sleep Classifiers by Genetic Algorithms, GECCO-99, Morgan Kaufmann Publishers, San Francisco, USA, 1999, pp. 1622-1629.
25. E. Noda, A.A. Freitas, H.S. Lopes, Discovering interesting prediction rules with a genetic algorithm, in: Proceedings of the Congress on Evolutionary Computation (CEC-99), Washington, DC, USA, 1999, pp. 1322-1329.
26. E. Noda, A.A. Freitas, A. Yamakami, A distributed-population genetic algorithm for discovering interesting prediction rules, in: Proceedings of the Seventh Online World Conference on Soft Computing (WSC7), Held on the Internet, 2002.
27. A. Papagelis, D. Kalles, Breeding decision trees using evolutionary techniques, in: Proceedings of the 18th International Conference on Machine Learning (ICML-2001), Morgan Kaufmann Publishers, San Francisco, USA, 2001, pp. 393-400.
28. J. Poon, S. Prasher, A hybrid approach to support interactive data mining, in: Proceedings of the Genetic and Evolutionary Computation Conference on GECCO'2001, Morgan Kaufmann Publishers, San Francisco, USA, 2001, pp. 527-534.
29. J.R. Quinlan, Generating production rules from decision trees, in: Proceedings of the IJCAI-87, Science & Technology Books, Milan, Italy, 1987, pp. 304-307.
30. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Francisco, USA, 1993.
31. W. Romão, A Genetic Algorithm for Discovering Interesting Fuzzy Prediction Rules: applications to science and technology data, in: W.B. Langdon, E. Cantu-Paz, et al. (Eds.), Proceedings of Genetic and Evolutionary Computation Conference (GECCO-2002), Morgan Kaufmann Publisher, San Francisco, CA, USA, 2002, pp. 1188-1195.
32. M. Setnes, H. Roubos, GA-fuzzy modeling and classification: complexity and performance, IEEE Trans. on Fuzzy Systems, vol. 8, no. 5, IEEE Computer Society, Los Alamitos, CA, USA, 2000, pp. 509-522.
33. A. Silberschatz, A. Tuzhilin, What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, IEEE Computer Society, Los Alamitos, CA, USA, 1996, pp. 970-974.
34. D. Walter, C.K. Mohan, ClaDia: a fuzzy classifier system for disease diagnosis, in: Proceedings of the Congress on Evolutionary Computation (CEC-2000), La Jolla, CA, USA, 2000.
35. G.J. Williams, Evolutionary hot spots data mining: an architecture for exploring for interesting discoveries, in: Proceedings of the third Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD'99), Springer, Berlin, 1999, pp. 184-193.
36. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, San Francisco, USA, 2000.
37. N. Xiong, L. Litz, Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms, in: Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery (PKDD-99), Springer, Berlin, 1999, pp. 574-579.