By Rainer Schmidt, Heike Weiss, Georg Fuellen (auth.), Petra Perner (eds.)
This booklet constitutes the refereed lawsuits of the twelfth business convention on information Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised complete papers offered have been rigorously reviewed and chosen from ninety seven submissions. The papers are equipped in topical sections on facts mining in drugs and biology; facts mining for power undefined; info mining in site visitors and logistic; information mining in telecommunication; facts mining in engineering; conception in information mining; conception in facts mining: clustering; conception in information mining: organization rule mining and choice rule mining.
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Extra resources for Advances in Data Mining. Applications and Theoretical Aspects: 12th Industrial Conference, ICDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings
Epidemiology of gastric cancer. World Journal of Gastroenterol 12, 354–362 (2006) 4. : Data Mining Introductory and Advanced Topics. Prentice Hall (2003) Research on Application of Data Mining Methods 37 5. : 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.
From the other hand, the speciﬁcity of classiﬁers remains on the level of 65-70%, which means that for about 30% of patients with negative diagnosis the decision support system suggested to make an endoscopy. This is a good result, comparing to the initial case, when an endoscopy was performed for each patient. Looking at the results from the other side - the false-negatives rate still remains above zero level. This means that some patiens with positive diagnosis will remain unthreated, meaning that the decision support system should not be used as a primary source for decision making.
After sorting, the values of cells in the same columns and subsequent rows were often equal, this is the key property of tick data that our approach exploits. Experimental Protocol — In our experiments we compared the decomposition of a tick data matrix resulting from the clusters produced by our approach, SOHAC, with the decomposition of the same tick data matrix using other clustering algorithms from the literature.