Classification, Clustering, and Data Mining Applications: by Banks D., et al. (eds.)

By Banks D., et al. (eds.)

This quantity describes new tools with detailed emphasis on type and cluster research. those tools are utilized to difficulties in info retrieval, phylogeny, scientific analysis, microarrays, and different lively learn parts.

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Additional info for Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies ... Data Analysis, and Knowledge Organization)

Example text

It is clear that the discretization technique has a significant impact of the size and accuracy of the decision trees. The experimental results shown in Table 8 suggest that an appropriate choice of β can reduce significantly the size and number of leaves of the decision trees, roughly maintaining the accuracy (measured by stratified five-fold cross validation) or even increasing the accuracy. Our supervised discretization algorithm that discretizes each attribute B based on the relationship between the partition πB and πA (where A is the attribute that specifies the class of the objects).

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2004). A greedy algorithm for supervised discretization. Journal of Biomedical Informatics, 285-292. Can, F. (1993). Incremental clustering for dynamic information processing. ACM Transactions for Information Systems, 11, 143-164. , Fox, E. , Snavely, C. D. & France, R. K. (1995). Incremental clustering for very large document databases: Initial {MARIAN} experience. Inf. , 84, 101-114. , & Grossberg, S. (1990). Art3: Hierachical search using chemical transmitters in self-organizing pattern recognition architectures.

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