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 used to be the 7th occasion within the commercial convention on facts Mining sequence and used to be held in Leipzig (www.data-mining-forum.de). For this variation this system Committee obtained ninety six submissions from 24 nations (see Fig. 1). After the peer-review strategy, we approved 25 fine quality papers for oral presentation which are integrated during this lawsuits e-book. the subjects variety from features of category and prediction, clustering, internet mining, info mining in medication, functions of information 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 displays which are released within the ICDM Poster lawsuits 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
It can be seen as a learning and knowledge-discovery approach, since it can capture from new experience some general knowledge, such as case classes, prototypes and some higher-level concept. We take case-based reasoning Concepts for Novelty Detection and Handling Based on a CBR Process Scheme 23 as the framework to solve our novelty detection problem under which we can run the different theoretical methods that should be used to detect the novel events and handle them. We chose a scenario for our study for which an attribute-value based representation is suitable.
The event is similar to existing events when the predictive probability-density function is high: p ( x / D ) = ∫ p (θ / D ) p ( x / θ )dθ (1) This Bayesian framework for making predictions can be used for all possible data models. Generally speaking, the integral in this equation is intractable. Several approximation methods exist, such as the Bayesian variational method , maximum a posteriori (MAP) method . Following the MAP approximation, we use a singlemind model θˆ that can maximize the posterior predictive probability distribution: p(θ / D ) .
In this scenario, old data might still be relevant, at least to some extent. Finally, one often speaks about virtual concept drift if not the concept itself changes 36 J. Beringer and E. H¨ ullermeier but the distribution of the underlying data generating process . Note that in practice virtual and real concept drift can occur simultaneously. Concept change can be handled in a direct or indirect way. In the indirect approach, the learning algorithm does not explicitly attempt to detect a concept drift.
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.)