Data Mining: STATISTICA

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1 Outline Data Mining: STATISTICA Prepare the data Classification and regression (C & R, ANN) Clustering Association rules Graphic user interface 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 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 1

2 Open an Excel File Change variable type Open an Excel File Change variable type Classification and Regression C&RT Classification Iris data is used as a example data set C&RT Boosting tree Neural Networks 2

3 C&RT Classification Click Data Mining menu and find the Interactive Trees C&RT Classification View the final tree and understand the results C&RT---Regression Use the CPU data set and select the regression analysis Regression tree structure C&RT---Regression 3

4 C&RT---Regression Boosting tree Classification In Data Mining menu and find the Boosted tree classifier and regression Predicted v alues Boosting tree Classification See the results and predictor s importance Boosting tree Classification See the results and predictor s importance 4

5 CPU data set Boosting tree Regression Boosting tree Classification See the results and predictor s importance Predicted v alues Boosting tree Classification See the results and predictor s importance Boosting tree Classification See the results and predictor s importance 5

6 Neural Networks Classification In Data Mining menu and find the Automated Neural Networks Neural Networks Classification Choose Classification, then select variables Neural Networks Classification Statistica will try a set of different neural networks and keep the best ones Neural Networks Classification See the classification results 6

7 Neural Networks Classification See the classification results---predictions Neural Networks Classification See the classification results---predictions Neural Networks Classification See the classification results---confusion matrix Neural Networks Regression CPU data set 7

8 Neural Networks Regression CPU data set, select variables Neural Networks Regression Training and results Neural Networks Regression Predictions Neural Networks Regression Some statistics about the predictions 8

9 Clustering Clustering Use the Deere data set Select k-means and choose the variables Clustering Choose the distance metrics and initial cluster centers 5 clusters and see the results Clustering 9

10 Centroids (cluster means) Clustering Clustering Members and their distance to the centroids Use the Deere data set Association rules Association rules Select variables and set up proper parameters 10

11 Association rules See rules Divide CPU data into training and testing data set Choose different algorithms 11

12 Insert the selected data mining algorithms into workspace Select data sources Specify whether the data is used to build the model or used as a testing set Connect the data with data mining algorithms 12

13 Connect the data with data mining algorithms Set up deployment, double click the data mining algorithm icon Click Run button See the deployment code by double click the icons in Reports section C code 13

14 Test the learnt models by testing data set First disable the connections between training data set and the data mining algorithms Connect the testing data set with the data mining algorithms Test the learnt models See the prediction results 14

Outline. Prepare the data Classification and regression Clustering Association rules Graphic user interface

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