dt mii statisticadata mining: statisticauser.engineering.uiowa.edu/~comp/public/statistica.pdf · 2...
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D t Mi i STATISTICAData Mining: STATISTICA
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Outline
•Prepare the data•Classification and regression•Clustering•Association rules •Graphic user interface
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•Graphic user interface
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Prepare the Data• Statistica can read from Excel, .txt and many other types of files• Compared with WEKA, Statistica is much easier in terms of data
preparing
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Open an Excel File• Click the “Import selected sheet to Spreadsheet”• Select the desired Excel sheet where your data is stored• Get variable names from the first row
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Open an Excel File• Change variable type
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Open an Excel File• Change variable type
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Classification and Regression
• C&RT• C&RT• Boosting tree• Neural Networks
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C&RT Classification• Iris data is used as a example data set
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C&RT Classification• Click “Data Mining” menu and find the “Interactive Trees”
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C&RT Classification• View the final tree and understand the results
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C&RT---Regression• Use the CPU data set and select the regression analysis
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C&RT---Regression• Regression tree structure
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C&RT---Regression
redi
cted
val
ues
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Boosting tree Classification• In “Data Mining” menu and find the “Boosted tree classifier and regression”
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Boosting tree Classification• See the results and predictor’s importance
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Boosting tree Classification• See the results and predictor’s importance
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Boosting tree Regression• CPU data set
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Boosting tree Classification• See the results and predictor’s importance
redi
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val
ues
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Boosting tree Classification• See the results and predictor’s importance
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Boosting tree Classification• See the results and predictor’s importance
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Neural Networks Classification• In “Data Mining” menu and find the “Automated Neural Networks”
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Neural Networks Classification• Choose “Classification”, then select variables
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Neural Networks Classification• Statistica will try a set of different neural networks and keep the best ones
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Neural Networks Classification• See the classification results
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Neural Networks Classification• See the classification results---Predictions
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Neural Networks Classification• See the classification results---Predictions
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Neural Networks Classification• See the classification results---Confusion matrix
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Neural Networks Regression• CPU data set
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Neural Networks Regression• CPU data set, select variables
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Neural Networks Regression• Training and results
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Neural Networks Regression• Predictions
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Neural Networks Regression• Some statistics about the predictions
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Clustering• Use the Deere data set
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Clustering• Select k-Means and choose the variables
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Clustering• Choose the distance metrics and initial cluster centers
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Clustering• 5 clusters and see the results
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Clustering• Centroids (cluster means)
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Clustering• Members and their distance to the centroids
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Association rules• Use the Deere data set
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Association rules• Select variables and set up proper parameters
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Association rules• See rules
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Graphic User Interface• Divide CPU data into training and testing data set
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Graphic User Interface
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Graphic User Interface• Choose different algorithms
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Graphic User Interface• Insert the selected data mining algorithms into workspace
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Graphic User Interface• Select data sources
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Graphic User Interface• Specify whether the data is used to build the model or used as a testing set
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Graphic User Interface• Connect the data with data mining algorithms
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Graphic User Interface• Connect the data with data mining algorithms
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Graphic User Interface• Set up deployment, double click the data mining algorithm icon
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Graphic User Interface• Click “Run” button
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Graphic User Interface• See the deployment code by double click the icons in “Reports” section
code
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Graphic User Interface• Test the learnt models by testing data set • First disable the connections between
t i i d t t d th d t i itraining data set and the data mining algorithms
• Connect the testing data set with the data mining algorithms
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Graphic User Interface• Test the learnt models
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Graphic User Interface• See the prediction results
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