標題: Titlebook: Core Concepts in Data Analysis: Summarization, Correlation and Visualization; Boris Mirkin Textbook 20111st edition Springer-Verlag London [打印本頁] 作者: 臉紅 時間: 2025-3-21 19:57
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書目名稱Core Concepts in Data Analysis: Summarization, Correlation and Visualization被引頻次
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書目名稱Core Concepts in Data Analysis: Summarization, Correlation and Visualization讀者反饋
書目名稱Core Concepts in Data Analysis: Summarization, Correlation and Visualization讀者反饋學(xué)科排名
作者: Neuropeptides 時間: 2025-3-21 20:53
1D Analysis: Summarization and Visualization of a Single Feature,ssible: just one feature. This also provides us with a stock of useful concepts for further material. The concepts of histogram, central point and spread are presented. Two perspectives on the summaries are outlined: one is the classical probabilistic and the other of approximation, naturally extend作者: 外面 時間: 2025-3-22 02:56 作者: 英寸 時間: 2025-3-22 06:27 作者: apropos 時間: 2025-3-22 11:33 作者: 單調(diào)性 時間: 2025-3-22 15:17 作者: 單調(diào)性 時間: 2025-3-22 19:37
Hierarchical Clustering, different algorithms for divisive clustering, all three based on the same square error criterion as K-Means partitioning method. Agglomerative clustering starts from a trivial set of singletons and merges two clusters at a time. Divisive clustering splits clusters in parts and should be a more inte作者: 對手 時間: 2025-3-22 23:33
Approximate and Spectral Clustering for Network and Affinity Data, chapter describes methods for finding a cluster or two-cluster split combining three types of approaches from both old and recent developments: (a)combinatorial approach that is oriented at clustering as optimization of some reasonable measure of cluster homogeneity, (b)additive clustering approach作者: Asperity 時間: 2025-3-23 01:45
Web Mining and Recommendation SystemsKohonen self-organizing maps (SOM) that tie up the sought clusters to a visually convenient two-dimensional grid. Equivalent reformulations of K-Means criterion are described – they can yield different algorithms for K-Means. One of these is explained at length: K-Means extends Principal component a作者: aplomb 時間: 2025-3-23 08:19
K-Means and Related Clustering Methods,Kohonen self-organizing maps (SOM) that tie up the sought clusters to a visually convenient two-dimensional grid. Equivalent reformulations of K-Means criterion are described – they can yield different algorithms for K-Means. One of these is explained at length: K-Means extends Principal component a作者: 保守黨 時間: 2025-3-23 11:37 作者: 格子架 時間: 2025-3-23 15:27 作者: 痛打 時間: 2025-3-23 18:18
Core Concepts in Data Analysis: Summarization, Correlation and Visualization作者: GLOOM 時間: 2025-3-24 00:33 作者: 不能逃避 時間: 2025-3-24 05:11
Learning Multivariate Correlations in Data,te measures described in Chapter 3– Quetelet indexes in contingency tables, first of all – and, second, normalization options for dummy variables representing target categories. Some related concepts such as Bayes decision rule, bag-of-word model in text analysis, VC-complexity and kernel for non-linear classification are introduced too.作者: 卡死偷電 時間: 2025-3-24 07:42
Textbook 20111st editioneither summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule)..Boris Mirkin takes an unconventional approach and introduces the concept of multi作者: hegemony 時間: 2025-3-24 14:15
Ling Shi,Lihua Xie,Richard M. Murrayot not. This difference is somewhat blurred at the binary features representing individual categories. They can be represented by the so-called dummy variables that can be considered quantitative too. Contemporary approaches, nature inspired optimization and bootstrap validation, are explained on individual cases.作者: Hiatus 時間: 2025-3-24 18:13
Payam Naghshtabrizi,Jo?o P. Hespanhaeatures rather than postulates it. Two more distant applications of PCA, Latent semantic analysis (for disambiguation in document retrieval) and Correspondence analysis (for visualization of contingency tables), are explained too. The issue of data standardization in data summarization problems, remaining unsolved, is discussed at length.作者: Negotiate 時間: 2025-3-24 20:48
Guandong Xu,Yanchun Zhang,Lin Lilits conceptually, that is, using one feature at a time. The last section is devoted to the Single Link clustering, a popular method for extraction of elongated structures from the data. Relations between single link clustering and two popular graph-theoretic structures, the Minimum Spanning Tree (MST) and connected components, are explained.作者: Addictive 時間: 2025-3-25 00:46 作者: 我不怕犧牲 時間: 2025-3-25 06:12 作者: 充氣球 時間: 2025-3-25 07:59
Hierarchical Clustering,lits conceptually, that is, using one feature at a time. The last section is devoted to the Single Link clustering, a popular method for extraction of elongated structures from the data. Relations between single link clustering and two popular graph-theoretic structures, the Minimum Spanning Tree (MST) and connected components, are explained.作者: ADAGE 時間: 2025-3-25 13:34
Annalisa Bonfiglio,Danilo De Rossiata analysis problems is presented. The datasets are taken from various fields such as monitoring market towns, computer security protocols, bioinformatics, cognitive psychology. (iii)An overview of data visualization, its goals and some techniques is given.作者: 說明 時間: 2025-3-25 17:48 作者: 絆住 時間: 2025-3-25 20:20 作者: Fermentation 時間: 2025-3-26 01:07
Introduction: What Is Core,ata analysis problems is presented. The datasets are taken from various fields such as monitoring market towns, computer security protocols, bioinformatics, cognitive psychology. (iii)An overview of data visualization, its goals and some techniques is given.作者: Processes 時間: 2025-3-26 07:12
2D Analysis: Correlation and Visualization of Two Features,dence, and Pearson’s chi-squared for two nominal variables; the latter is treated as a summary correlation measure, in contrast to the conventional view of it as a criterion of statistical independence. They all are applicable in the case of multidimensional data as well.作者: Temporal-Lobe 時間: 2025-3-26 09:36 作者: Abutment 時間: 2025-3-26 16:03
1863-7310 d to date..Explores methodical innovations of summarization .Core Concepts in Data Analysis: Summarization, Correlation and Visualization. .provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical作者: 乏味 時間: 2025-3-26 19:38
https://doi.org/10.1007/978-0-85729-287-2Clustering; Data Analysis; K-means; Principal component analysis; Visualization作者: palliative-care 時間: 2025-3-27 00:26 作者: Hdl348 時間: 2025-3-27 04:01 作者: 圖畫文字 時間: 2025-3-27 07:29 作者: Fibrin 時間: 2025-3-27 10:48 作者: CLAN 時間: 2025-3-27 15:38
Muhammad Tahir,Sudip K. Mazumderr decision rule building. Two of them pertain to quantitative targets (linear regression, neural network), and four to categorical ones (linear discrimination, support vector machine, na?ve Bayes classifier and classification tree). Of these, classification trees are treated in a most detailed way i作者: 怒目而視 時間: 2025-3-27 21:36 作者: Aggrandize 時間: 2025-3-27 22:31 作者: instate 時間: 2025-3-28 03:36
Guandong Xu,Yanchun Zhang,Lin Li different algorithms for divisive clustering, all three based on the same square error criterion as K-Means partitioning method. Agglomerative clustering starts from a trivial set of singletons and merges two clusters at a time. Divisive clustering splits clusters in parts and should be a more inte作者: 阻礙 時間: 2025-3-28 07:12 作者: 掃興 時間: 2025-3-28 10:57
Boris MirkinProvides an in-depth understanding of a few basic techniques in data analysis rather than covering the broad spectrum of approaches developed to date..Explores methodical innovations of summarization 作者: overbearing 時間: 2025-3-28 15:39 作者: 財產(chǎn) 時間: 2025-3-28 21:19
Core Concepts in Data Analysis: Summarization, Correlation and Visualization978-0-85729-287-2Series ISSN 1863-7310 Series E-ISSN 2197-1781 作者: 龍蝦 時間: 2025-3-29 01:13
Quasiexperimentelle Feldstudie zur Beanspruchung von Montagemitarbeitern,2). Darauf aufbauend erfolgt im Abschnitt 3.3 die Darstellung des Versuchsdesigns, bestehend aus den Erl?uterungen zur Stichprobe, der Beschreibung der untersuchten Arbeitspl?tze, der Vorstellung der verwendeten Messmethoden und -instrumente sowie der Bericht zur Versuchsdurchführung.
