Rainer Schmidt, Heike Weiss, Georg Fuellen (auth.), Petra's Advances in Data Mining. Applications and Theoretical PDF

By Rainer Schmidt, Heike Weiss, Georg Fuellen (auth.), Petra Perner (eds.)

ISBN-10: 3642314872

ISBN-13: 9783642314872

ISBN-10: 3642314880

ISBN-13: 9783642314889

This booklet constitutes the refereed lawsuits of the twelfth commercial convention on facts Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised complete papers provided have been rigorously reviewed and chosen from ninety seven submissions. The papers are equipped in topical sections on facts mining in medication and biology; facts mining for power undefined; information mining in site visitors and logistic; info mining in telecommunication; info mining in engineering; idea in info mining; thought in info mining: clustering; thought in info mining: organization rule mining and determination rule mining.

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Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann (2006) 6. : Global cancer statistics. CA: A Cancer Journal for Clinicians 61, 69–91 (2011) 7. : Rule induction for subgroup discovery with CN2-SD. In: 2nd International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning, pp. 77–81. University of Helsinki (2002) 8. : Subgroup discovery with CN2-SD. Journal of Machine Learning Research 5, 153–188 (2004) 9. : Data Preparation for Data Mining.

Fig. 4. Classification tree 36 A. Kirshners, S. Parshutin, and M. Leja not have other tumours AND Have no heartburn AND 1st-degree relatives have gastric cancer THEN result is Negative. This points out that classifiers are data specific and may contain rules that are normal for a training set, but do not concur with an opinion of a medical expert. That is one of the reasons for system to be a decision support not a decision system. 4 Conclusions Gastric cancer is not the only disease the presented system can be used for.

First, we motivate our approach with an illustrative example, then we develop a new clustering algorithm that supports efficient storage of tick data. 1 An Illustrative Example Suppose that a weather station monitors features of weather conditions. org SOHAC: Efficient Storage of Tick Data 41 Fig. 1. An illustrative example for tick data. Features describing the weather are monitored continuously. Whenever the value of one of the features changes, a new row is inserted into the recordings (see the table in the top).

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Advances in Data Mining. Applications and Theoretical Aspects: 12th Industrial Conference, ICDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings by Rainer Schmidt, Heike Weiss, Georg Fuellen (auth.), Petra Perner (eds.)


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