Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20203rd edition Springer Fachmedien Wiesbaden GmbH, part of Springer Nature

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978-3-658-29778-7Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020

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https://doi.org/10.1007/978-1-0716-3678-7, 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

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Mei-Sheng Xiao,Jeremy E. Wiluszdered 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 matrix norms, Lebesgue/Minkowski norms) or on similarity measures (like cosine, overla

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Suman Singh,Aniruddha Das,Amaresh C. Pandably heterogeneous information sources. We distinguish deterministic and stochastic errors. Deterministic errors can sometimes be easily corrected. Inliers and outliers may be identified and removed or corrected. Inliers, outliers, or noise can be reduced by filtering. We distinguish many different f

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Circular RNAs Act as miRNA Spongesy 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