Abstract
A pipelined approach using two clustering algorithms in combination with Rough Sets is investigated for the purpose discovering important combination of attributes in high dimensional data. In many domains, the data objects are described in terms of a large number of features, like in gene expression experiments, or in samples characterized by spectral information. The Leader and several k-means algorithms are used as fast procedures for attribute set simplification of the information systems presented to the rough sets algorithms. The data submatrices described in terms of these features are then discretized w.r.t the decision attribute according to different rough set based schemes. From them, the reducts and their derived rules are extracted, which are applied to test data in order to evaluate the resulting classification accuracy. An exploration of this approach (using Leukemia gene expression data) was conducted in a series of experiments within a high-throughput distributed-computing environment. They led to subsets of genes with high discrimination power. Good results were obtained with no preprocessing applied to the data.
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References
Hartigan, J.: Clustering Algorithms. John Wiley & Sons, Chichester (1975)
Anderberg, M.: Cluster Analysis for Applications. Academic Press, London (1973)
Gower, J.C.: A general coefficient of similarity and some of its properties. Biometrics 1(27), 857–871 (1973)
Chandon, J.L., Pinson, S.: Analyse typologique. Théorie et applications: Masson, Paris (1981)
Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht (1991)
Bazan, J.G., Skowron, A., Synak, P.: Dynamic Reducts as a Tool for Extracting Laws from Decision Tables. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 346–355. Springer, Heidelberg (1994)
Wróblewski, J.: Ensembles of Classifiers Based on Approximate Reducts. Fundamenta Informaticae 47, 351–360 (2001)
Valdés, J.J.: Similarity-Based Heterogeneous Neurons in the Context of General Observational Models. Neural Network World 12(5), 499–508 (2002)
Valdés, J.J.: Virtual Reality Representation of Relational Systems and Decision Rules: An exploratory Tool for understanding Data Structure. In: Hajek, P. (ed.) Theory and Application of Relational Structures as Knowledge Instruments. Meeting of the COST Action, Prague, November 14-16, vol. 274 (2002)
Borg, I., Lingoes, J.: Multidimensional similarity structure analysis. Springer, New York (1987)
Sammon, J.W.: A non-linear mapping for data structure analysis. IEEE Trans. on Computers C18, 401–409 (1969)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Øhrn, A., Komorowski, J.: Rosetta- A Rough Set Toolkit for the Analysis of Data. In: Proc. of Third Int. Join Conf. on Information Sciences (JCIS97), Durham, NC, USA, March 1-5, pp. 403–407 (1997)
Valdés, J.J., Barton, A.J.: Gene Discovery in Leukemia Revisited: A Computational Intelligence Perspective. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 118–127. Springer, Heidelberg (2004)
Famili, F., Ouyang, J.: Data mining: understanding data and disease modeling. In: Proceedings of the 21st IASTED International Conference, Applied Informatics, Innsbruck, Austria, February 10-13, vol. 37 (2003)
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Valdés, J.J., Barton, A.J. (2005). Relevant Attribute Discovery in High Dimensional Data Based on Rough Sets and Unsupervised Classification: Application to Leukemia Gene Expressions. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_38
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DOI: https://doi.org/10.1007/11548706_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
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