By Petra Perner
This ebook constitutes the refereed complaints of the sixth business convention on facts Mining, ICDM 2006, held in Leipzig, Germany in July 2006. offers forty five conscientiously reviewed and revised complete papers prepared in topical sections on info mining in medication, net mining and logfile research, theoretical features of knowledge mining, facts mining in advertising and marketing, mining indications and photographs, and features of information mining, and purposes reminiscent of intrusion detection, and extra.
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Extra resources for Advances in Data Mining: Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 2006, Proceedings
2. 3. 4. 5. Concatenate a set of homologous genes of a group of organisms into a supergene G . Encoding the super-gene G into a corresponding digit X to be mined by a selforganizing map. Conduct the SOM mining for the numeric matrix X . Computing gene distribution p' ( x ) on the SOM plane for each gene x by retrieving the frequency of sites in the gene hitting their best match unit (BMU) on the SOM plane. Estimating the multispecies gene x entropy by Equation 6. 42 X. g M simply. The super-gene is called a m × n character matrix for the convenience of discussion.
Teng is more effective than the approach used in Case 2 at identifying truly relevant features. In this particular study, Case 1 represents the OVA version of the DDP-based feature selection technique, and Case 2, the SMA version. 7 Conclusions Based on one or more of the following criteria: class accuracy, best averaged accuracy and size-averaged accuracy, the OVA version of the DDP-based feature selection technique outperforms the SMA version. Despite the increase in computational cost and predictor set size by a factor of K, the improvement brought by the OVA scheme in terms of overall accuracy and class accuracy is especially significant for the datasets with the largest number of classes and highest level of complexity and difficulty, such as the GCM and NCI60 datasets.
Feature selection for high-dimensional genomic microarray data. In: Proc. 18th International Conference on Machine Learning (2001) 601–608 8. : Relevance, redundancy and differential prioritization in feature selection for multiclass gene expression data. S. ): Proc. 6th International Symposium on Biological and Medical Data Analysis (ISBMDA-05) (2005) 367–378 9. : Large margin DAGs for multiclass classification. Advances in Neural Information Processing Systems 12 (2000) 547–553 10. : Machine Learning, McGraw-Hill, 1997 11.
Advances in Data Mining: Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 2006, Proceedings by Petra Perner