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

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

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 business convention on info Mining sequence and used to be held in Leipzig ( For this variation this system Committee got ninety six submissions from 24 nations (see Fig. 1). After the peer-review method, we permitted 25 fine quality papers for oral presentation which are incorporated during this complaints booklet. the themes diversity from elements of class and prediction, clustering, internet mining, facts mining in drugs, purposes of knowledge mining, time sequence and common development 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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The new data model ~ Θ is obtained by updating the previous model Θ , when new data is added to a new class and eventually to existing ones. Θ = (θ , θ M +1 ) , where θ is the data model obtained during the previous update and modified to fulfill any additional constraints due to the adding of a new cluster. For example, in the case of a mixture model, adding a new cluster implies that the summation of mixing parameters must be normalized such that it is equal to one. θ M +1 is the data model of the new class, computed by using a moment method or any other available method.

The statistical learning unit takes this case class and proves, based on the MMLcriterion, if it is suitable to learn a new model. In case the statistical component recommends not to learn a new model, the case-class is still hosted by the case-base maintenance unit and further up-dated, based on new observed events that might change the inner-class structure, as long as there is new evidence to learn a statistical model. The similarity-based reasoning unit and the statistical models also act together on the reasoning level.

A very early approach is the FLORA (Floating Rough Approximation) system [30]. The corresponding algorithm learns rule-based binary classifiers on a sliding window of fixed size. The FRANN (Floating Rough Approximation in Neural Networks) algorithm trains RBF networks on a sliding window of adaptive size [22]. The LWF (Locally Weighted Forgetting) algorithm of Salganicoff [25] is among the best adaptive learning algorithms. It is an instancebased learner that reduces the weights of the k nearest neighbors x1 .

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