標(biāo)題: Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20162nd edition Springer Fachmedien Wiesbaden 2016 data mining.knowledge di [打印本頁] 作者: Holter-monitor 時(shí)間: 2025-3-21 18:54
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: Mirage 時(shí)間: 2025-3-21 21:46
Clustering,Complex relational clusters can be found by kernelization. Cluster tendency assessment finds out if the data contain clusters at all, and cluster validity measures help identify an appropriate number of clusters. Clustering can also be done by heuristic methods such as the self-organizing map.作者: Iatrogenic 時(shí)間: 2025-3-22 03:27 作者: AIL 時(shí)間: 2025-3-22 04:43
Data and Relations,rlap, Dice, Jaccard, Tanimoto). Sequences can be analyzed using sequence relations (like Hamming, Levenshtein, edit distance). Data can be extracted from continuous signals by sampling and quantization. The Nyquist condition allows sampling without loss of information.作者: 決定性 時(shí)間: 2025-3-22 12:06
Data Preprocessing, different effectiveness and computational complexities: moving statistical measures, discrete linear filters, finite impule response, infinite impulse response. Data features with different ranges often need to be standardized or transformed.作者: 能得到 時(shí)間: 2025-3-22 16:31 作者: 能得到 時(shí)間: 2025-3-22 18:08 作者: grandiose 時(shí)間: 2025-3-22 21:44
Classification,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 regression trees.作者: CANDY 時(shí)間: 2025-3-23 04:25
ment 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 results of industrial research and development projects at Siemens.978-3-658-14075-5作者: 適宜 時(shí)間: 2025-3-23 09:05 作者: TOXIC 時(shí)間: 2025-3-23 13:02 作者: Coronation 時(shí)間: 2025-3-23 15:04
Conceptualizing the Circular Economyta sets for forecasting models are generated by finite unfolding in time. Popular linear forecasting models are auto-regressive models (AR) and generalized AR models with moving average (ARMA), with integral terms (ARIMA), or with local regression (ARMAX). Popular nonlinear forecasting models are recurrent neural networks.作者: 同來核對(duì) 時(shí)間: 2025-3-23 18:51 作者: MUMP 時(shí)間: 2025-3-23 23:39 作者: Obsessed 時(shí)間: 2025-3-24 02:59 作者: medieval 時(shí)間: 2025-3-24 10:09
https://doi.org/10.1007/978-981-99-3083-8rlap, Dice, Jaccard, Tanimoto). Sequences can be analyzed using sequence relations (like Hamming, Levenshtein, edit distance). Data can be extracted from continuous signals by sampling and quantization. The Nyquist condition allows sampling without loss of information.作者: ENNUI 時(shí)間: 2025-3-24 11:37 作者: 土產(chǎn) 時(shí)間: 2025-3-24 18:47
Changiz Valmohammadi,Farkhondeh Mortaz Hejriinear projection methods (Sammon mapping, auto-associator). Data distributions can be estimated and visualized using histogram techniques. Periodic data (such as time series) can be analyzed and visualized using spectral analysis (cosine and sine transforms, amplitude and phase spectra).作者: Incumbent 時(shí)間: 2025-3-24 21:59
Che?ma Fersi,Ilhem Ben Salah,Raouf Medimaghelation can also be quantified by the regression validation error. Correlation does not imply causality, so correlation analysis may reveal spurious correlations. If the underlying features are known, then spurios correlations may be compensated by partial correlation methods.作者: 誘騙 時(shí)間: 2025-3-24 23:34 作者: 終止 時(shí)間: 2025-3-25 04:56 作者: CHASE 時(shí)間: 2025-3-25 07:41 作者: 英寸 時(shí)間: 2025-3-25 12:17 作者: Feigned 時(shí)間: 2025-3-25 19:01 作者: MIRTH 時(shí)間: 2025-3-25 20:05 作者: 攤位 時(shí)間: 2025-3-26 00:15
Changiz Valmohammadi,Farkhondeh Mortaz Hejri. To visualize high-dimensional data, projection methods are necessary. We present linear projection (principal component analysis, Karhunen-Loève transform, singular value decomposition, eigenvector projection, Hotelling transform, proper orthogonal decomposition, multidimensional scaling) and nonl作者: placebo 時(shí)間: 2025-3-26 07:33 作者: Bernstein-test 時(shí)間: 2025-3-26 09:22 作者: 缺陷 時(shí)間: 2025-3-26 14:20
Conceptualizing the Circular Economyy 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: minaret 時(shí)間: 2025-3-26 20:02 作者: MODE 時(shí)間: 2025-3-26 23:29
Sadhan Kumar Ghosh,Sannidhya Kumar Ghoshes, the clusters may or may not correspond with the physical classes. Cluster partitions may be mathematically represented by sets, partition matrices, and/or cluster prototypes. Sequential clustering (single linkage, complete linkage, average linkage, Ward’s method, etc.) is easily implemented but 作者: Gustatory 時(shí)間: 2025-3-27 02:09
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: 金絲雀 時(shí)間: 2025-3-27 07:51 作者: monochromatic 時(shí)間: 2025-3-27 11:46 作者: 書法 時(shí)間: 2025-3-27 16:03
Springer Fachmedien Wiesbaden 2016作者: 無能力 時(shí)間: 2025-3-27 18:21 作者: Phagocytes 時(shí)間: 2025-3-27 21:58
Data and Relations,nted 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 matrix norms, Lebesgue/Minkowski norms) or on similarity measures (like cosine, ove作者: gain631 時(shí)間: 2025-3-28 03:41 作者: LAPSE 時(shí)間: 2025-3-28 09:45
Data Visualization,. To visualize high-dimensional data, projection methods are necessary. We present linear projection (principal component analysis, Karhunen-Loève transform, singular value decomposition, eigenvector projection, Hotelling transform, proper orthogonal decomposition, multidimensional scaling) and nonl作者: 防御 時(shí)間: 2025-3-28 10:24
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: GROG 時(shí)間: 2025-3-28 16:32
Regression,d to linear dependencies. Substitution allows us to identify specific nonlinear dependencies by linear regression. Robust regression finds models that are robust against outliers. A popular family of nonlinear regression methods are universal approximators. We present two well-known examples for uni作者: 小母馬 時(shí)間: 2025-3-28 22:39
Forecasting,y 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: 隼鷹 時(shí)間: 2025-3-29 00:05
Classification, 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 作者: commune 時(shí)間: 2025-3-29 05:31
