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Record Nr. |
UNINA9910826809903321 |
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Autore |
Myatt Glenn J. <1969-> |
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Titolo |
Making sense of data II : a practical guide to data visualization, advanced data mining methods, and applications / / Glenn J. Myatt, Wayne P. Johnson |
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Pubbl/distr/stampa |
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Hoboken, N.J., : John Wiley & Sons, c2009 |
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ISBN |
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9786612031076 |
9781282031074 |
1282031074 |
9780470417409 |
0470417404 |
9780470417393 |
0470417390 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (307 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Data mining |
Information visualization |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. 273-277) and index. |
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Nota di contenuto |
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MAKING SENSE OF DATA II; CONTENTS; PREFACE; 1 INTRODUCTION; 1.1 Overview; 1.2 Definition; 1.3 Preparation; 1.3.1 Overview; 1.3.2 Accessing Tabular Data; 1.3.3 Accessing Unstructured Data; 1.3.4 Understanding the Variables and Observations; 1.3.5 Data Cleaning; 1.3.6 Transformation; 1.3.7 Variable Reduction; 1.3.8 Segmentation; 1.3.9 Preparing Data to Apply; 1.4 Analysis; 1.4.1 Data Mining Tasks; 1.4.2 Optimization; 1.4.3 Evaluation; 1.4.4 Model Forensics; 1.5 Deployment; 1.6 Outline of Book; 1.6.1 Overview; 1.6.2 Data Visualization; 1.6.3 Clustering; 1.6.4 Predictive Analytics |
1.6.5 Applications1.6.6 Software; 1.7 Summary; 1.8 Further Reading; 2 DATA VISUALIZATION; 2.1 Overview; 2.2 Visualization Design Principles; 2.2.1 General Principles; 2.2.2 Graphics Design; 2.2.3 Anatomy of a Graph; 2.3 Tables; 2.3.1 Simple Tables; 2.3.2 Summary Tables; 2.3.3 Two-Way Contingency Tables; 2.3.4 Supertables; 2.4 Univariate Data Visualization; 2.4.1 Bar Chart; 2.4.2 Histograms; 2.4.3 |
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Frequency Polygram; 2.4.4 Box Plots; 2.4.5 Dot Plot; 2.4.6 Stem-and-Leaf Plot; 2.4.7 Quantile Plot; 2.4.8 Quantile-Quantile Plot; 2.5 Bivariate Data Visualization; 2.5.1 Scatterplot |
2.6 Multivariate Data Visualization2.6.1 Histogram Matrix; 2.6.2 Scatterplot Matrix; 2.6.3 Multiple Box Plot; 2.6.4 Trellis Plot; 2.7 Visualizing Groups; 2.7.1 Dendrograms; 2.7.2 Decision Trees; 2.7.3 Cluster Image Maps; 2.8 Dynamic Techniques; 2.8.1 Overview; 2.8.2 Data Brushing; 2.8.3 Nearness Selection; 2.8.4 Sorting and Rearranging; 2.8.5 Searching and Filtering; 2.9 Summary; 2.10 Further Reading; 3 CLUSTERING; 3.1 Overview; 3.2 Distance Measures; 3.2.1 Overview; 3.2.2 Numeric Distance Measures; 3.2.3 Binary Distance Measures; 3.2.4 Mixed Variables; 3.2.5 Other Measures |
3.3 Agglomerative Hierarchical Clustering3.3.1 Overview; 3.3.2 Single Linkage; 3.3.3 Complete Linkage; 3.3.4 Average Linkage; 3.3.5 Other Methods; 3.3.6 Selecting Groups; 3.4 Partitioned-Based Clustering; 3.4.1 Overview; 3.4.2 k-Means; 3.4.3 Worked Example; 3.4.4 Miscellaneous Partitioned-Based Clustering; 3.5 Fuzzy Clustering; 3.5.1 Overview; 3.5.2 Fuzzy k-Means; 3.5.3 Worked Examples; 3.6 Summary; 3.7 Further Reading; 4 PREDICTIVE ANALYTICS; 4.1 Overview; 4.1.1 Predictive Modeling; 4.1.2 Testing Model Accuracy; 4.1.3 Evaluating Regression Models' Predictive Accuracy |
4.1.4 Evaluating Classification Models' Predictive Accuracy4.1.5 Evaluating Binary Models' Predictive Accuracy; 4.1.6 ROC Charts; 4.1.7 Lift Chart; 4.2 Principal Component Analysis; 4.2.1 Overview; 4.2.2 Principal Components; 4.2.3 Generating Principal Components; 4.2.4 Interpretation of Principal Components; 4.3 Multiple Linear Regression; 4.3.1 Overview; 4.3.2 Generating Models; 4.3.3 Prediction; 4.3.4 Analysis of Residuals; 4.3.5 Standard Error; 4.3.6 Coefficient of Multiple Determination; 4.3.7 Testing the Model Significance; 4.3.8 Selecting and Transforming Variables |
4.4 Discriminant Analysis |
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Sommario/riassunto |
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A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction tha |
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