Titlebook: Advanced Data Mining and Applications; 9th International Co Hiroshi Motoda,Zhaohui Wu,Wei Wang Conference proceedings 2013 Springer-Verlag

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Berichte zur Lebensmittelsicherheit 2008o large scale data sets because it needs O(..) computational operations to process a data set of . data points[1]. Based on the minimization of the increment of distortion, we tackle this problem by developing a novel efficient growing vector quantization method to preprocess a large scale data set,

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Berichte zur Lebensmittelsicherheit 2008e databases. One of those approaches, the DARA algorithm, is designed to transform data stored in relational databases into a vector space representation utilising information retrieval theory. The DARA algorithm has shown to produce improvements over other state-of-the-art approaches. However, the

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https://doi.org/10.1007/978-3-0346-0205-1datable by inserting new transactions and it provides efficient querying and updating algorithms. However, an important limitation of the IT structure, concerning scalability, is that it consumes a large amount of memory. In this paper, we address this limitation by proposing an improved data struct

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Berichte zur Lebensmittelsicherheit 2008nal (e.g., human) intervention. A .-dominating set in a distributed system is a set of processors such that each processor outside the set has at least . neighbors in the set. In the past, a few self-stabilizing algorithms for minimal .-dominating set (MKDS) have been obtained. However, the presente

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Berichte zur Lebensmittelsicherheit 2008when the data sets are imbalanced, the probability of rare event is underestimated in the use of traditional logistic regression. With data explosion in recent years, some researchers propose large scale logistic regression which still fails to consider the rare event, therefore, there exists bias w