By Rainer Schmidt, Heike Weiss, Georg Fuellen (auth.), Petra Perner (eds.)
This booklet constitutes the refereed court cases of the twelfth business 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 geared up in topical sections on facts mining in medication and biology; information mining for power undefined; information mining in site visitors and logistic; info mining in telecommunication; facts mining in engineering; concept in facts mining; idea in facts mining: clustering; conception in information mining: organization rule mining and selection rule mining.
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Our approach is described in Section 3, followed by our experiments in Section 4. Finally, we conclude in Section 5. 2 Related Work The availability of high-resolution data describing transactions on ﬁnancial markets, especially tick data (also known as tick-by-tick data) allows thorough analysis of the markets and their dynamics. Some of the most relevant recent works focused for example on currency exchange rates , , , , stock market tick data , risk analysis  and and the dynamics of stock markets .
The main objective of the proposed system is to lessen the false positive rate, simultaneously maintaining high sensitivity. Returning to the initial dataset the classes are highly imbalanced, which may lead to incorrectly interpreted results. All three classiﬁers were trained using all 819 examples with full feature set and tested using the 10-fold cross-validation; the results of experiments are given in Table 1. All classiﬁers show a classiﬁcation accuracy greater than 95%, but the sensitivity (true positive rate) of a target class - positive diagnosis, remains 0 or close to it, pointing out that classiﬁers were not able to correctly classify examples with a positive diagnosis.
I has also compared these themes with those in the CBR conference literature, and found both common elements and differences. This analysis of CBR-HS literature also permits to identify potential future research directions. Future directions include visualization and evolution tracking of CBR-HS literature, comparison with automatic classification, as well automatizing the indexing system as much as feasible. References 1. : Methodology for Classifying and Indexing Case-Based Reasoning Systems in the Health Sciences.
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.)