Gene discovery in leukemia revisited: A computational intelligence perspective

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3029, Issue , 2004, Pages 118-127

  Valdés, Julio J ^a   Barton, Alan J ^a  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

Clustering; Computational intelligence; Data mining; Feed forward and probabilistic neural networks; Leukemia; Rough sets; Virtual reality

Indexed keywords

DATA MINING; GENES; NEURAL NETWORKS; PROBABILITY; PROBLEM SOLVING; VIRTUAL REALITY; ARTIFICIAL INTELLIGENCE; DISEASES; EXPERT SYSTEMS; ROUGH SET THEORY;

CLUSTERING; FEED FORWARD AND PROBABILISTIC NEURAL NETWORKS; LEUKEMIA; ROUGH SETS; GENE DISCOVERY; LEUKEMIA DATA; NUMBER OF CLUSTERS; PROBABILISTIC NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE; GENES;

EID: 9444238576     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-24677-0_13     Document Type: Conference Paper

References (16)

1. Anderberg, M.: Cluster Analysis for Applications. Academic Press, 359 pp, 1973.
2. Bazan, J.G., Szczuka S., Wroblewski, J: A New Version of Rough Set Exploration System. Third. Int. Conf. on Rough Sets and Current Trends in Computing RSCTC 2002. Malvern, PA, USA, Oct 14-17. Alpigini, Peters, Skowron, Zhong (Eds.) Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence Series) LNCS 2475, pp. 397-404. Springer-Verlag, 2002.
3. Borg, I., and Lingoes, J., Multidimensional similarity structure analysis: Springer-Verlag, New York, NY, 390 p. 1987.
4. Chandon, J.L., and Pinson, S., Analyse typologique. Théorie et applications: Masson, Paris, 254 p. 1981.
5. Deb, K and Reddy, A. R. Classification of Two-Class Cancer Data Reliably Using Evolutionary Algorithms. KanGAL Report No. 2003001. http://www.iitk.ac.in/ kangal/pub.htm February, 2003.
6. Famili, F. and Ouyang, J., Data mining: understanding data and disease modeling. In Proceedings of the 21st IASTED International Conference, Applied Informatics, Innsbruck, Austria, pp. 32-37. Feb. 10-13, 2003.
7. Golub, T.R., etal. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, vol. 286, pp531-537. 1999.
8. Gower, J.C., A general coefficient of similarity and some of its properties: Biometrics, v. 1, no. 27, p. 857-871. 1973.
9. Hartigan, J.: Clustering Algorithms. John Wiley & Sons, 351 pp, 1975.
10. Masters, T.: Advanced Algorithms for Neural Networks. John Wiley & Sons, pp 431,1993.
11. Pawlak, Z., Rough sets: Theoretical aspects of reasoning about data: Kluwer Academic Publishers, Dordrecht, Netherlands, 229 p. 1991.
12. Sammon, J.W. A non-linear mapping for data structure analysis. IEEE Trans, on Computers CIS, p 401-409. 1969.
13. Son H. Nguyen, Hoa S. Nguyen. Discretization Methods in Data Mining. In: L. Polkowski, A. Skowron (eds.): Rough Sets in Knowledge Discovery. Physica-Verlag, Heidelberg, pp. 451-482. 1998.
14. Specht, D.: Probabilistic Neural Networks. Neural Networks 3, pp 109-118, 1990.
15. Valdés, J.J.: Virtual Reality Representation of Relational Systems and Decision Rules: An exploratory Tool for understanding Data Structure. In Theory and Application of Relational Structures as Knowledge Instruments. Meeting of the COST Action 274 (P. Hajek. Ed). Prague, November 14-16, 2002.
16. Valdés, J.J : Similarity-Based Heterogeneous Neurons in the Context of General Observational Models. Neural Network World. Vol 12, No. 5, pp 499-508, 2002.