By Rainer Schmidt, Heike Weiss, Georg Fuellen (auth.), Petra Perner (eds.)
This booklet constitutes the refereed complaints of the twelfth commercial convention on info Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised complete papers offered have been conscientiously reviewed and chosen from ninety seven submissions. The papers are equipped in topical sections on facts mining in medication and biology; info mining for power undefined; information mining in site visitors and logistic; information mining in telecommunication; info mining in engineering; thought in info mining; concept in facts mining: clustering; conception in information mining: organization rule mining and selection rule mining.
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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
Nagy and K. Buza Fig. 2. Construction of a binary change indicator matrix from a tick data matrix. The tick data matrix is shown in the top of the ﬁgure, while the corresponding indicator matrix is shown in the bottom. The index column is the Time column in this example. , all the columns except the index column) as instances in conventional clustering algorithms. t. the required storage space compared to the case of storing the original tick data matrix. In the next section, we develop a clustering algorithm that directly optimize the storage space required to store the decomposed tick data matrix.
Hu Abstract. Storage of tick data is a challenging problem because two criteria have to be fulﬁlled simultaneously: the storage structure should allow fast execution of queries and the data should not occupy too much space on the hard disk or in the main memory. In this paper, we present a clustering-based solution, and we introduce a new clustering algorithm that is designed to support the storage of tick data. We evaluate our algorithm both on publicly available real-world datasets, as well as real-world tick data from the ﬁnancial domain provided by one of the world-wide most renowned investment bank.
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.