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

ISBN-10: 3540734341

ISBN-13: 9783540734345

ICDM / MLDM Medaillie (limited variation) Meissner Porcellan, the “White Gold” of King August the most powerful of Saxonia ICDM 2007 was once the 7th occasion within the commercial convention on information Mining sequence and used to be held in Leipzig ( For this variation this system Committee got ninety six submissions from 24 international locations (see Fig. 1). After the peer-review technique, we authorised 25 fine quality papers for oral presentation which are incorporated during this lawsuits booklet. the subjects variety from elements of class and prediction, clustering, net mining, information mining in drugs, purposes of information mining, time sequence and widespread 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 international locations Twelve papers have been chosen for poster shows which are released within the ICDM Poster complaints Volume.

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

Example text

The sample distribution is shown in table 2. Only database DB_1 can be used for the learning of a statistical model, since the four classes have a sufficiently large number of samples. Class 1 to 4 in database DB2-DB4 can be used to update the statistical model. All the other classes in the databases should be considered as novel events that occur in sequence. 30 P. Perner The evaluation of our method is not easy because of the underlying different aspects. These aspects are: 1. up-dating existing concepts to achieve a better performance of the model or to handle the concept drift, 2.

To this end, 1,000 extra test instances are generated at random according to a uniform distribution, and the classification accuracy for this test set is derived by using the current case base; this is done for every 10 time points. We conducted experiments with 8 different data streams (see table 1), some of which have already been used in the literature before: The streams Gauss, Sine2, Stagger and Mixed were used in [14], and the Hyperplane data (that we generated for the dimensions d = 2 and d = 5) was used in multiple experiments for data streams in [28].

First of all, in order to guarantee that results are always up-to-date, it is necessary to analyze the incoming data in an online manner, tolerating not more than a constant time delay. Since learning from scratch every time is generally excluded due to limited time and memory resources, corresponding learning algorithms must be incremental. , they must be able to adapt to an evolving environment in which the data (stream) generating process may change over time. Thus, the handling of changing concepts is of utmost importance in mining data streams [5].

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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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