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Titlebook: Advanced Data Mining and Applications; 6th International Co Longbing Cao,Yong Feng,Jiang Zhong Conference proceedings 2010 Springer Berlin

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Classification Inductive Rule Learning with Negated Featuresicular product is being made geographically, at any point in the world, similar conditions of material and quality will prevail. In many cases the machinery of manufacture will be of similar characteristics; indeed, in some industries the machinery of production will be virtually identical, there be
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Fast Retrieval of Time Series Using a Multi-resolution Filter with Multiple Reduced Spacess, which have been collected by the authors in nearly 2000 surveys. The key part consists of approx. 130 association tables presenting plant sociological data for the respective plant communities. Thus, this volume perfectly complements the successful main volume “Plant Life of the Dolomites: Vegeta
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DHPTID-HYBRID Algorithm: A Hybrid Algorithm for Association Rule Mininghroughout the Dolomites.Complements the two former volumes o.This volume offers distribution maps of over 2200 individual species living in the Dolomite area, presenting detailed records on the local range of every species growing in the area studied, from the Puster Valley to the Piave River. The d
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Discretization of Time Series Dataset Using Relative Frequency and K-Nearest Neighbor ApproachgyWhen starting our work on this book we intended to summarize and synthesize the new information that had developed in the last 20-30 years in the field of plant lit­ ter decomposition. It turned out, however, that the main part of more recent work was directed towards boreal and temperate forest s
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MSDBSCAN: Multi-density Scale-Independent Clustering Algorithm Based on DBSCANmus layer and the other a theoretical study based on calculable stable residue (limit-value approach). For both studies, the country was subdivided into grid cells and Kriging interpolation was used. In the first case study, Kriging was used to interpolate humus-layer thickness over 41 years, giving
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https://doi.org/10.1007/978-3-642-17316-5Clustering; HPC; adaptive algorithms; classification; data mining; data types; graphs; knowledge discovery;
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