Making sense of data II: a pratical guide to data visualization, advanced data mining methods, and applications / (Record no. 3571)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05501nam a2200145 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780470222805 (pb) |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CUS |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.74 |
| Item number | MYA/M |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Myatt, Glenn J. |
| 245 ## - TITLE STATEMENT | |
| Title | Making sense of data II: a pratical guide to data visualization, advanced data mining methods, and applications / |
| Statement of responsibility, etc. | Glenn J, Myatt and Wayne P. Johnson |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | New Jersey : |
| Name of publisher, distributor, etc. | John wiley, |
| Date of publication, distribution, etc. | 2009. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xi, 291 p. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1.3.1 Overview<br/>1.3.2 Accessing Tabular Data<br/>1.3.3 Accessing Unstructured Data<br/>1.3.4 Understanding the Variables and Observations<br/>1.3.5 Data Cleaning<br/>1.3.6 Transformation<br/>1.3.7 Variable Reduction<br/>1.3.8 Segmentation<br/>1.3.9 Preparing Data to Apply<br/>1.4 Analysis<br/>1.4.1 Data Mining Tasks<br/>1.4.2 Optimization<br/>1.4.3 Evaluation<br/>1.4.4 Model Forensics<br/>1.5 Deployment<br/>1.6 Outline of Book<br/>1.6.1 Overview<br/>1.6.2 Data Visualization<br/>1.6.3 Clustering<br/>1.6.4 Predictive Analytics<br/>1.6.5 Applications<br/>1.6.6 Software<br/>1.7 Summary<br/>1.8 Further Reading<br/>2 DATA VISUALIZATION<br/>2.1 Overview<br/>2.2 Visualization Design Principles<br/>2.2.1 General Principles<br/>2.2.2 Graphics Design<br/>2.2.3 Anatomy of a Graph<br/>2.3 Tables<br/>2.3.1 Simple Tables<br/>2.3.2 Summary Tables<br/>2.3.3 Two-Way Contingency Tables<br/>2.3.4 Supertables<br/>2.4 Univariate Data Visualization<br/>2.4.1 Bar Chan<br/>2.4.2 Histograms<br/>2.4.3 Frequency Polygram<br/>2.4.4 Box Plots<br/>2.4.5 Dot Plot<br/>2.4.6 Stem-and-Leaf Plot<br/>2.4.7 Quantile Plot<br/>2.4.8 Quantile-Quanlile Plot<br/>2.5 Bivariate Data Visualization<br/>2.5.1 Scatterplot<br/>2.6 Multivariate Data Visualization<br/>2.6.1 Histogram Matrix<br/>2.6.2 Scatterplot Matrix<br/>2.6.3 Multiple Box Plot<br/>2.6.4 Trellis Plot<br/>2.7 Visualizing Groups<br/>2.7.1 Dendrograms<br/>2.7.2 Decision Trees<br/>2.7.3 Cluster Image Maps<br/>2.8 Dynamic Techniques<br/>2.8.1 Overview<br/>2.8.2 Data Brushing<br/>2.8.3 Nearness Selection<br/>2.8.4 Sorting and Rearranging<br/>2.8.5 Searching and Filtering<br/>2.9 Summary<br/>2.10 Further Reading<br/>3 CLUSTERING<br/>3.1 Overview<br/>3.2 Distance Measures<br/>3.2.1 Overview<br/>3.2.2 Numeric Distance Measures<br/>3.2.3 Binary Distance Measures<br/>3.2.4 Mixed Variables<br/>3.2.5 Other Measures<br/>3.3 Agglomerative Hierarchical Clustering<br/>3.3.1 Overview<br/>3.3.2 Single Linkage<br/>3.3.3 Complete Linkage<br/>3.3.4 Average Linkage<br/>3.3.5 Other Methods<br/>3.3.6 Selecting Groups<br/>3.4 Partilloncd-Bascci Clustering<br/>3.4.1 Overview<br/>3.4.2 A-Mcans<br/>3.4.3 Worked Example<br/>3.4.4 Miscellaneous Partitioned-Based Clustering<br/>3.5 Fuzzy Clustering<br/>3.5.1 Overview<br/>3.5.2 Fuzzy A-Means<br/>3.5.3 Worked Examples<br/>3.6 Summary<br/>3.7 Further Reading<br/>4 PREDICTIVE ANALYTICS<br/>4.1 Overview<br/>4.1.1 Predictive Modeling<br/>4.1.2 Testing Model Accuracy<br/>4.1.3 Evaluating Regression Models' Predictive Accuracy<br/>4.1.4 Evaluating Classification Models' Predictive Accuracy<br/>4.1.5 Evaluating Binary Models' Predictive Accuracy<br/>4.1.6 ROC Charts<br/>4.1.7 Lilt Chan<br/>4.2 Principal Component Analysis<br/>4.2.1 Overview<br/>4.2.2 Principal Components<br/>4.2.3 Generating Principal Components<br/>4.2.4 Interpretation of Principal Components<br/>4.3 Multiple Linear Regression<br/>4.3.1 Overview<br/>4.3.2 Generating Models<br/>4.3.3 Prediction<br/>4.3.4 Analysis of Residuals<br/>4.3.5 Standard Error<br/>4.3.6 Coefficient of Multiple Determination<br/>4.3.7 Testing tho Model Significance<br/>4.3.8 Selecting and Transforming Variables<br/>4.4 Di.scriminant Analysis<br/>4.4.1 Overview<br/>4.4.2 Discriminant Function<br/>4.4.3 Discriminant Analysis Example<br/>4.5 Logistic Regression<br/>4.5.1 Overview<br/>4.5.2 Logistic Regression Formula<br/>4.5.3 Estimating Coefficients<br/>4.5.4 Assessing and Optimizing Results<br/>4.6 Naive Baycs Classifiers<br/>4.6.1 Overview<br/>4.6.2 Bayes Theorem and the Independence Assumption<br/>4.6.3 Independence Assumption<br/>4.6.4 Classification Process<br/>VIII CONTENTS<br/>4.7 Summary<br/>4.8 Further Reading<br/>5 APPLICATIONS<br/>5.1 Overview<br/>5.2 Sales and Marketing<br/>5.3 Industry-Specific Data Mining<br/>5.3.1<br/>Finance<br/>5.3.2<br/>Insurance<br/>5.3.3<br/>Retail<br/>5.3.4<br/>Telecommunications<br/>5.3.5<br/>Manufacturing<br/>5.3.6<br/>Entertainment<br/>5.3.7<br/>Government<br/>5.3.8<br/>Pharmaceuticals<br/>5.3.9<br/>Healthcare<br/>5.4 microRNA Data Analysis Case Study<br/>5.4.1 Defining the Problem<br/>5.4.2 Preparing the Data<br/>5.4.3 Analysis<br/>5.5 Cnsdit Scoring Case Study<br/>5.5.1 Defining the Problem<br/>5.5.2 Preparing the Data<br/>5.5.3 Analysis<br/>5.5.4 Deployment<br/>5.6 Data Mining Nontabular Data<br/>5.6.1 Overview<br/>5.6.2 Data Mining Chemical Data<br/>5.6.3 Data Mining Text<br/>5.7 Further Reading<br/>APPENDIX A MATRICES<br/>A.l Overview of Matrices<br/>A.2 Matrix Addition<br/>A.3 Matrix Multiplication<br/>A.4 Transpose of a Matrix<br/>A.5 Inverse of a Matrix<br/>APPENDIX B SOFTWARE<br/>B.l Software Overview<br/>B. 1.1 Software Objectives<br/>B.l.2 Access and Installation<br/>B. 1.3 User Interface Overview<br/>B.2 Data Preparation<br/>B.2.1 Overview<br/>B.2.2 Reading in Data<br/>B.2.3 Searching the Data<br/>B.2.4 Variable Characterization<br/>B.2.5 Removing Observations and Variables<br/>B.2.6 Cleaning the Data<br/>B.2.7 Transforming the Data<br/>B.2.8 Segmentation<br/>B.2.9 Principal Component Analysis<br/>B.3 Tables and Graphs<br/>B.3.1 Overview<br/>B.3.2 Contingency Tables<br/>B.3.3 Summary Tables<br/>B.3.4 Graphs<br/>B.3.5 Graph Matrices<br/>B.4 Statistics<br/>B.4.1 Overview<br/>B.4.2 Descriptive Statistics<br/>B.4.3 Confidence Intervals<br/>B.4.4 Hypothesis Tests<br/>B.4.5 Chi-Squarc Test<br/>B.4.6 ANOVA<br/>B.4.7 Comparative Statistics<br/>B.5 Grouping<br/>B.5.1 Overview<br/>B.5.2 Clustering<br/>B.5.3 Associative Rules<br/>B.5.4 Decision Trees<br/>B.6 Prediction<br/>B.6.1 Overview<br/>B.6.2 Linear Regression<br/>B.6.3 Discriminant Analysis<br/>B.6.4 Logistic Regression<br/>B.6.5 Naive Bayes<br/>B.6.6 ANN<br/>B.6.7 CART<br/>B.6.8 Neural Networks<br/>B.6.9 Apply Model |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | GN Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Full call number | Accession number | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Central Library, Sikkim University | Central Library, Sikkim University | General Book Section | 24/06/2016 | 005.74 MYA/M | P30999 | 24/06/2016 | General Books |