New PDF release: Advances in Data Mining. Applications and Theoretical

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

ISBN-10: 3642314872

ISBN-13: 9783642314872

ISBN-10: 3642314880

ISBN-13: 9783642314889

This publication 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 awarded have been conscientiously reviewed and chosen from ninety seven submissions. The papers are prepared in topical sections on info mining in drugs and biology; information mining for strength undefined; information mining in site visitors and logistic; info mining in telecommunication; facts mining in engineering; thought in info mining; thought in info mining: clustering; conception in facts mining: organization rule mining and determination rule mining.

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Read Online or Download Advances in Data Mining. Applications and Theoretical Aspects: 12th Industrial Conference, ICDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings PDF

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Extra info for Advances in Data Mining. Applications and Theoretical Aspects: 12th Industrial Conference, ICDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings

Example text

The ‘#’ symbol refers to the ranking in either the CBR conference literature [8] (2nd column) or the CBR-HS core literature (4th column). As for CBR conference literature main research themes not represented on Table 6, they are of interest for suggesting future research themes in CBR-HS: • • • Recommender systems & diversity: diversity is an important aspect for differential diagnosis. Even though a few CBR-HS systems show some interest in this direction, it is a promising topic to focus on for the future.

5 CN2 Rules classifiers have shown an increase in average sensitivity using the Second feature set with eight attributes and the value remains greater than 50%, however the average false negative rate remains above the 20% level. 1 datasets (see Table 3) showed that sampling and feature selection can increase an efficiency of classifier, however the results show a high variance in estimations, especially in sensitivity. The main reason of that is the small number of examples in each subset, comparing to the initial dataset.

1 Experimental Settings Datasets — We tested our approach both on a real-world tick data describing financial transactions and several publicly available real-world dataset. The real-world tick data from the financial domain was provided us by Morgan Stanley, one of the most renowned investment bank of the world. Therefore, in this paper, we call this dataset MorganStanleyTickData. 431 rows. I. Nagy and K. Buza Additionally, we used publicly-available real-world datasets: we used some of the most popular datasets from the UCI machine learning repository [8].

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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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