Get Advances in Data Mining. Applications and Theoretical PDF

By Giorgio Giacinto (auth.), Petra Perner (eds.)

ISBN-10: 3642144004

ISBN-13: 9783642144004

These are the court cases of the 10th occasion of the economic convention on facts Mining ICDM held in Berlin (www.data-mining-forum.de). For this version this system Committee got one hundred seventy five submissions. After the pe- evaluation strategy, we approved forty nine fine quality papers for oral presentation which are incorporated during this ebook. the themes diversity from theoretical facets of information mining to app- cations of knowledge mining reminiscent of on multimedia info, in advertising, finance and telec- munication, in drugs and agriculture, and in technique keep watch over, and society. prolonged types of chosen papers will look within the overseas magazine Trans- tions on computer studying and knowledge Mining (www.ibai-publishing.org/journal/mldm). Ten papers have been chosen for poster displays and are released within the ICDM Poster continuing quantity by way of ibai-publishing (www.ibai-publishing.org). along with ICDM 4 workshops have been hung on distinctive scorching applicati- orientated issues in facts mining: info Mining in advertising and marketing DMM, info Mining in LifeScience DMLS, the Workshop on Case-Based Reasoning for Multimedia info CBR-MD, and the Workshop on info Mining in Agriculture DMA. The Workshop on information Mining in Agriculture ran for the 1st time this yr. All workshop papers should be released within the workshop complaints via ibai-publishing (www.ibai-publishing.org). chosen papers of CBR-MD can be released in a distinct factor of the foreign magazine Transactions on Case-Based Reasoning (www.ibai-publishing.org/journal/cbr).

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Extra info for Advances in Data Mining. Applications and Theoretical Aspects: 10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings

Sample text

Phylogenetic trees represent the evolutions of populations, while in examples from other domains classify individuals [20]. In addition, applications are often interested in finding explanations for what is observed, while in evolution, it is mostly classification that is of interest [20]. Nevertheless, researchers who have used phylogenetic classification in other domains have published their findings because they found them interesting: “The Darwinian mechanisms of selection and transmission, when incorporated into an explanatory theory, provide precisely what culture historians were looking for: the tools to begin explaining cultural lineages—that is, to answer why-type questions” [18].

Microarray data present a particular challenge for data miners, known as the curse of dimensionality. These datasets often comprise from tens to hundreds of samples or cases for thousands to tens of thousands of predictor genes. In this context, identifying a subset of genes the most connected with the outcome studied has been shown to provide better results – both in classification and in prediction. Therefore feature selection methods have been developed with the goal of selecting the smallest subset of genes providing the best classification or prediction.

In: Proceedings of the Ninth National Conference on Artificial Intelligence, pp. 547–552. AAAI Press, Menlo Park (1991) 9. : Correlation-based feature selection for discrete and numeric class machine learning. In: Proceeding of the 17th International Conference on Machine Learning, pp. 359–366. Morgan Kaufmann, San Francisco (2000) 10. : Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004) 11. : Efficient dimensionality reduction approaches for feature selection.

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Advances in Data Mining. Applications and Theoretical Aspects: 10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings by Giorgio Giacinto (auth.), Petra Perner (eds.)


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