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 information Mining sequence and used to be held in Leipzig (www.data-mining-forum.de). For this variation this system Committee bought ninety six submissions from 24 international locations (see Fig. 1). After the peer-review procedure, we authorised 25 top of the range papers for oral presentation which are incorporated during this complaints booklet. the themes variety from elements of type and prediction, clustering, net mining, facts 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 shows which are released within the ICDM Poster court cases Volume.
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Additional info for Advances in Data Mining. Theoretical Aspects and Applications: 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007. Proceedings
The Prediction Error Context Switching algorithm (PECS), also proposed in , does not delete but only deactivates instances. That is, removed instances are still stored in memory and might be reactivated later on. This strategy can avoid some disadvantages of LWF but entails storage requirements that disqualify PECS for the data stream context. In the above approaches, the strategies for adapting the size of a sliding window, if any, are mostly of a heuristic nature. In , the authors propose to adapt the window size in such a way as to minimize the estimated generalization error 38 J.
These aspects are: 1. up-dating existing concepts to achieve a better performance of the model or to handle the concept drift, 2. the recognition of novel events, 3. reasoning over novel events and 4. the learning of new concepts. At the recent status we can only give an outline of the evaluation procedure and the concept behind. These four tasks might be influenced by different factors: 1. Up-dating existing Concepts 2. There must be some influence of the sample distribution. 3. How many samples are necessary for evaluating the recent performance of the model?
2 New Proposal for Novelty Detection and Handling We propose novelty detection to be seen as a case-based reasoning problem . According to our understanding of the novelty detection problem, the case-based reasoning process, with its different tasks, has all the functions necessary for handling novelty detection in an efficient way and it satisfies the incremental nature that it is up to many real-world problems. CBR solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem.