DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining with Rattle Introduction Data Mining with Rattle Data Sources Selecting Data Exploring Data Transforming Data Descriptive Models Predictive Models Evaluation and Deployment Issues Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

List of Figures

1. The Rattle window.
2. Initial steps of the data mining process (Tony Nolan)
3. The data mining process
4. The Rattle window showing paradigms
5. Selecting the Unsupervised paradigm
6. A sample of plots
7. Rattle title bar showing the file name
8. The Rattle window.
9. The CSV file chooser
10. After identifying a file to load
11. Data tab dataset summary.
12. Loading an ARFF file
13. Loading data through an ODBC database connection
14. Teradata ODBC connection
15. Netezza ODBC connection
16. Netezza configuration
17. Loading an R binary data file.
18. Loading an already defined R data frame
19. Selected region of a spreadsheet copied to the clipboard
20. Loading an R data frame originally from the clipboard
21. Data entry spreadsheet
22. Select tab choosing Adjusted as a Risk variable.
23. Missing value summary for a version of the audit modified to include missing values.
24. Benford stratified by Marital and Gender.
25. Mosaic plot of Age by Adjusted.
26. Correlations between keywords in documents.
27. Transform options.
28. Selection of normalisations performed on Income.
29. Normalisations of Age.
30. Normalisations of Age.
31. Selection of imputations.
32. Imputation using the mode for missing values of Age.
33. Binning Age.
34. Distributions of binned Age.
35. Turning Gender into an Indicator Variable.
36. Selection of cleanup operations.
37. KMeans Iteration Interface
38. KMeans Iteration Plot
39. Random forest tuning parameters.
40. Random forests only supports factors with up to 32 levels.
41. Random forest model of audit data.
42. Random forest model measure of variable importance.
43. Random forest risk charts: test and train datasets.
44. Warning when evaluating a model on the training dataset.
45. Random forest ROC chart.
46. Informational dialog.
47. Evaluate tab with Score option and a CSV file.
48. Scores have been saved.
49. Load and analyse score data using the Gnumeric spreadsheet.
50. Distribution of scores displayed using Rattle.
51. R command line under GNU/Linux
52. R command line under MS/Windows
53. R GUI using ESS for Emacs
54. R Commander GUI
55. An ordered monthly box plot.
56. A approximate model of random data.
57. Reduced example of an alternating decision tree.
58. Audit risk chart from an alternating decision tree.
59. Togaware's Rattle Gnome Data Mining interface.
60. The Weka GUI chooser.
61. Weka explorer viewing data.
62. Import CSV data into Weka.
63. Output from running J48 (C4.5).
64. Fujitsu GhostMiner interface.
65. Sample ODMiner interface to ODM.
66. SAS Enterprise Miner interface (Version 4).
67. Statistica Data Miner graphical interface.

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