標(biāo)題: Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20203rd edition Springer Fachmedien Wiesbaden GmbH, part of Springer Nature [打印本頁] 作者: 桌前不可入 時(shí)間: 2025-3-21 19:55
書目名稱Data Analytics影響因子(影響力)
書目名稱Data Analytics影響因子(影響力)學(xué)科排名
書目名稱Data Analytics網(wǎng)絡(luò)公開度
書目名稱Data Analytics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Analytics被引頻次
書目名稱Data Analytics被引頻次學(xué)科排名
書目名稱Data Analytics年度引用
書目名稱Data Analytics年度引用學(xué)科排名
書目名稱Data Analytics讀者反饋
書目名稱Data Analytics讀者反饋學(xué)科排名
作者: 駁船 時(shí)間: 2025-3-21 23:50 作者: 反叛者 時(shí)間: 2025-3-22 01:56
Data Preprocessing,bly 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作者: 替代品 時(shí)間: 2025-3-22 05:24 作者: 說笑 時(shí)間: 2025-3-22 09:06
Correlation,ependencies. Nonlinear correlation methods 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 independence that can be applied to continuous features using histogram counts. Nonlinear corr作者: Condyle 時(shí)間: 2025-3-22 13:47
Regression,d to linear dependencies. Substitution allows to identify specific types of nonlinear dependencies by linear regression. Robust regression finds models that are robust against inliers or outliers. A popular class of nonlinear regression methods are universal approximators. We present two well-known 作者: Condyle 時(shí)間: 2025-3-22 20:23 作者: Judicious 時(shí)間: 2025-3-22 23:39 作者: Crohns-disease 時(shí)間: 2025-3-23 02:41 作者: 從容 時(shí)間: 2025-3-23 07:07 作者: 商業(yè)上 時(shí)間: 2025-3-23 10:02 作者: 悶熱 時(shí)間: 2025-3-23 17:42 作者: cushion 時(shí)間: 2025-3-23 20:42
Data and Relations,p, Dice, Jaccard, Tanimoto). Sequences can be analyzed using sequence relations (like Hamming or edit distance). Data can be extracted from continuous signals by sampling and quantization. The Nyquist condition allows sampling without loss of information.作者: 偶像 時(shí)間: 2025-3-23 22:49 作者: 溫和女孩 時(shí)間: 2025-3-24 03:26 作者: 后天習(xí)得 時(shí)間: 2025-3-24 07:22
Correlation,elation can also be quantified by the cross-validation error of regression models. Correlation does not imply causality. Spurious correlations may lead to wrong conclusions. If the underlying features are known, then spurious correlations may be compensated by partial correlation methods.作者: 陳腐思想 時(shí)間: 2025-3-24 14:41 作者: Chromatic 時(shí)間: 2025-3-24 18:39 作者: 撤退 時(shí)間: 2025-3-24 21:33
Circularity Assessment: Macro to Nano data, often enhanced by kernelization. Cluster tendency assessment finds out if the data contain clusters at all, and cluster validity measures help identify the number of clusters or other algorithmic parameters. Clustering can also be done by heuristic methods such as self-organizing maps.作者: 內(nèi)行 時(shí)間: 2025-3-25 00:55
Clustering, data, often enhanced by kernelization. Cluster tendency assessment finds out if the data contain clusters at all, and cluster validity measures help identify the number of clusters or other algorithmic parameters. Clustering can also be done by heuristic methods such as self-organizing maps.作者: INERT 時(shí)間: 2025-3-25 05:37 作者: 藕床生厭倦 時(shí)間: 2025-3-25 10:35 作者: 大吃大喝 時(shí)間: 2025-3-25 14:11 作者: deforestation 時(shí)間: 2025-3-25 16:03 作者: BAIL 時(shí)間: 2025-3-25 23:50
Suman Singh,Aniruddha Das,Amaresh C. Pandailtering methods with different effectiveness and computational complexities: moving statistical measures, discrete linear filters, finite impulse response, infinite impulse response. Data features with different ranges often need to be standardized or transformed.作者: Debrief 時(shí)間: 2025-3-26 01:16 作者: 制造 時(shí)間: 2025-3-26 07:51 作者: ingrate 時(shí)間: 2025-3-26 10:23
https://doi.org/10.1007/978-981-13-1426-1bilities and limitations are presented in detail: the naive Bayes classifier, linear discriminant analysis, the support vector machine (SVM) using the kernel trick, nearest neighbor classifiers, learning vector quantification, and hierarchical classification using decision trees.作者: instill 時(shí)間: 2025-3-26 16:07
Textbook 20203rd editionvantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the re作者: Banquet 時(shí)間: 2025-3-26 19:30 作者: 小蟲 時(shí)間: 2025-3-26 21:58 作者: 反感 時(shí)間: 2025-3-27 04:16 作者: Forehead-Lift 時(shí)間: 2025-3-27 05:24
978-3-658-29778-7Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020作者: 辯論 時(shí)間: 2025-3-27 10:26
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作者: Hirsutism 時(shí)間: 2025-3-27 16:38
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作者: 被告 時(shí)間: 2025-3-27 21:06
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作者: Irritate 時(shí)間: 2025-3-27 23:22 作者: GET 時(shí)間: 2025-3-28 06:02 作者: 盡管 時(shí)間: 2025-3-28 06:29 作者: 尊嚴(yán) 時(shí)間: 2025-3-28 11:54
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作者: 稀釋前 時(shí)間: 2025-3-28 15:40 作者: Myocarditis 時(shí)間: 2025-3-28 20:10 作者: 向外 時(shí)間: 2025-3-29 00:16
Thomas A. RunklerA comprehensive introduction.Enabling the reader to design and implement data analytics solutions for real-world applications.Written by a researcher from industry with substantial experience with rea作者: 有組織 時(shí)間: 2025-3-29 04:55
