Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20121st edition Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden 2012

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书目名称Data Analytics影响因子(影响力)




书目名称Data Analytics影响因子(影响力)学科排名




书目名称Data Analytics网络公开度




书目名称Data Analytics网络公开度学科排名




书目名称Data Analytics被引频次




书目名称Data Analytics被引频次学科排名




书目名称Data Analytics年度引用




书目名称Data Analytics年度引用学科排名




书目名称Data Analytics读者反馈




书目名称Data Analytics读者反馈学科排名

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https://doi.org/10.1007/978-0-387-88849-1, image data, and biomedical data. We define the terms data analytics, data mining, knowledge discovery, and the KDD and CRISP-DM processes. Typical data analysis projects can be divided into several phases: preparation, preprocessing, analysis, and postprocessing. The chapters of this book are stru

Binge-Drinking

The Many Faces of the Single-Tuned Circuitnted for because certain mathematical operations are only appropriate for specific scales. Numerical data can be represented by sets, vectors, or matrices. Data analysis is often based on dissimilarity measures (like inner product norms, Lebesgue/Minkowski norms) or on similarity measures (like cosi

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Circuits, Systems and Signal Processing heterogeneous information sources.We distinguish deterministic and stochastic errors. Deterministic errors can sometimes be easily corrected. Outliers need to be identified and removed or corrected. Outliers or noise can be reduced by filtering. We distinguish many different filtering methods with

Nutrient

Basic Concepts in Signals and Systems. To visualize high-dimensional data, projection methods are necessary. We present linear projection (principal component analysis, Karhunen-Lo`eve transform, singular value decomposition, eigenvector projection, Hotelling transform, proper orthogonal decomposition) and nonlinear projection methods

Nutrient

The Circular Economy and Business Challengesinear correlation methods are robust and computationally efficient but detect only linear dependencies. Nonlinear correlationmethods are able to detect nonlinear dependencies but need to be carefully parametrized. As a popular example for nonlinear correlation we present the chi-square test for inde

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Sergei Yu. Venyaminov,Jen Tsi Yangy or a Moore machine. This leads to recurrent or auto-regressive models. Building forecasting models is essentially a regression task. The training data sets for forecasting models are generated by finite unfolding in time. Popular linear forecasting models are auto-regressive models (AR) and genera

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https://doi.org/10.1007/978-981-19-0549-0 define numerous indicators to quantify classifier performance. Pairs of indicators are considered to assess classification performance.We illustrate this with the receiver operating characteristic and the precision recall diagram. Several different classifiers with specific features and drawbacks a