Download e-book for iPad: Advances in Data Mining. Theoretical Aspects and by Michael M. Richter (auth.), Petra Perner (eds.)

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By Michael M. Richter (auth.), Petra Perner (eds.)

ISBN-10: 3540734341

ISBN-13: 9783540734345

ICDM / MLDM Medaillie (limited version) Meissner Porcellan, the “White Gold” of King August the most powerful of Saxonia ICDM 2007 used to be the 7th occasion within the business convention on facts Mining sequence and used to be held in Leipzig ( For this version this system Committee acquired ninety six submissions from 24 international locations (see Fig. 1). After the peer-review strategy, we accredited 25 top quality papers for oral presentation which are incorporated during this lawsuits e-book. the themes variety from facets of class and prediction, clustering, internet mining, info mining in drugs, functions of knowledge mining, time sequence and common trend mining, and organization rule mining. Germany 9,30% 4,17% China 9,30% 1,04% 6,98% 3,13% South Korea Czech Republic 6,98% 3,13% united states 6,98% 2,08% 4,65% 2,08% united kingdom Portugal 4,65% 2,08% Iran 4,65% 2,08% India 4,65% 2,08% Brazil 4,65% 1,04% Hungary 4,65% 1,04% Mexico 4,65% 1,04% Finland 2,33% 1,04% eire 2,33% 1,04% Slovenia 2,33% 1,04% France 2,33% 1,04% Israel 2,33% 1,04% Spain 2,33% 1,04% Greece 2,33% 1,04% Italy 2,33% 1,04% Sweden 2,33% 1,04% Netherlands 2,33% 1,04% Malaysia 2,33% 1,04% Turkey 2,33% 1,04% Fig. 1. Distribution of papers between nations Twelve papers have been chosen for poster shows which are released within the ICDM Poster complaints Volume.

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Extra info for Advances in Data Mining. Theoretical Aspects and Applications: 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007. Proceedings

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Therefore, the choice is to use similarity-based reasoning and to collect the new events in a hierarchical fashion, based on a similarity relation, into the case base. Similar cases representing one new event should be grouped together in a case class, and new events making up a new case class should be inserted into the case base by taking into account the similarity relation to other case classes. Therefore, the similarity-based reasoning unit is comprised of two functions: 1. reasoning over novel events and 2.

The number of instances or subnodes of a node is restricted to an interval [minCapacity, maxCapacity]. Our experience has shown that the interval [6, 15] yields good performances. The stored examples correspond to the instance nodes of the tree (located directly under the leaf nodes), the radius of which is 0. To delete data with preference to older instances, the number of items to be removed in a node is uniformly spread among the subnodes. In a leaf, only the 42 J. Beringer and E. H¨ ullermeier oldest instances are removed.

This has something to do with false/positive recognition 6. Learning New Concepts 7. How many samples are necessary to learn novel concepts? According to the literature [25] approximately 100 samples are needed. 8. How can the case base organization control the learning of the models? Table 1. Name of Database and Number of Classes and Samples per Class Name DB_1 DB_2 DB_3 DB_4 1 2 3 4 5 6 7 8 Class Number Number 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 of Classes 7 5 15 9 5 105 96 63 83 8 2 2 14 4 14 7 30 29 28 11 7 13 5 13 13 25 12 18 21 5 16 22 24 21 20 9 5 14 8 10 48 23 17 31 3 3 3 7 5 2 13 5 Number of Cases 4 347 26 298 10 12 156 203 The evaluation of the statistical model can only be done by test-and-train based on a large enough test data set (500 samples according to [25]) which is not available in the proposed kind of application.

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Advances in Data Mining. Theoretical Aspects and Applications: 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007. Proceedings by Michael M. Richter (auth.), Petra Perner (eds.)

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