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Lab Manual Prepared by: Prof (Dr). V. SARAVANAN,School of Computer Applications, LPU

S.No TITLE

1 IMPORTING DATA INTO RAPIDMINER

2 STORING AND RETRIEVING DATA

3 GRAPHICAL REPRESENTATION OF DATA

4 APPLYING MODEL FOR PREDICTION

5IMPLEMENTATION OF BAYESIAN MODEL ON

IMPORTED DATA

6 CROSS VALIDATION

7CREATION OF GENERIC OPTIMIZATION

PREPROCESSOR

8 REPRESENTING DATA USING DECISION TREE

9EVOLUTIONARY WEIGHTING OF THE

ATTRIBUTES

10 TEXTMINING USING RAPIDMINER

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IMPORTING DATA INTO RAPIDMINER

AIM:To import files into RapidMiner tool using three methods.

ALGORITHM:

Step 1: Open the RapidMiner tool.Step 2: In the welcome perspective that opens, select the new icon.

Method 1:Step 3: Select the repository location and click ok.

Step 4: In the design perspective, click operators view import data Read Excel.

Step 5: The Read Excel operator will be displayed in the process view.

Step 6: Connect the out of the operator to the res of the process.

Step 7: Click on the operator to view the parameters and browse the file to be imported.

Step 8: Run the process. The result can be viewed in metadata, data and plot views.

Method 2:Step 9: In the design perspective, click on the repositories view.

Step 10: Click on the Import drop down, select import Excel sheet.Step 11: Select the file to be imported and` click Next.

Step 12: Specify a repository location, click Finish and view the result.

Method 3:Step 13: Drag the file to be imported and drop it in the NewLocalRepository of the repositories

View and click Next.

Step 14: Specify a repository location, click Finish and view the result.

SCREENSHOTS:Method 1:

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Method 2:

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Method 3:

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RESULT:The files are imported into the RapidMiner tool using the three methods.

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STORING AND RETRIEVING DATA

AIM:To store and retrieve data into RapidMiner tool.

ALGORITHM:Step 1: Open the RapidMiner tool.

Step 2: In the welcome perspective that opens, select the new icon.

Step 3: Select the repository location and click ok.

Step 4: In the design perspective, click operators view import data Read Excel.

Step 5: The Read Excel operator will be displayed in the process view.

Step 6: In the design perspective, click operators view Repository AccessStore.

Step 7: The Store operator will be displayed in the process view.

Step 8: Connect the out of the operator to the inp of the Store and thr of store to the res of

process.

Step 9: Click on the operator to view the parameters and browse the file to be imported.

Step 10: Run the process. The result can be viewed in metadata, data and plot views.

SCREENSHOTS:Storing data:

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Retrieving data:

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RESULT:The data are stored and retrieved into the RapidMiner tool.

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GRAPHICAL REPRESENTATION OF DATA

AIM:To graphically represent the data

ALGORITHM:

STEP 1: Open the rapid miner tool.STEP2: Select file->new, Repository browser dialog box appears , In that selectthe

repository and give a name and click ok.STEP 3:In the operator window select import->data->read excel.STEP 4:In the parameter window .select the excel file and click open.STEP 5:Run the process.STEP 6: Select the plot viewSTEP 7:Select the type of plotter and set the parameters.STEP 8: The corresponding graph appear will appear in the right side window.

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SCREEN SHOTS:

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RESULT:Thus the data will be displayed graphically successfully.

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APPLYING MODEL FOR PREDICTIONAIM:

To predict the unknown data using neural net and apply models in RapidMiner tool.

ALGORITHM:Step 1: Open the RapidMiner tool.

Step 2: In the welcome perspective that opens, select the new icon.

Step 3: Select the repository location and click ok.

Step 4: Import two csv files containing the mark and result details.

Step 5: The result field of first file is given as label and of the second as prediction.

Step 6: Retrieve both the files into the design view of the process.

Step 7: In the design perspective, click operators view Modeling Classification and

RegressionNeural Net trainingNeural Net.

Step 8: In the design perspective, click operators view ModelingModel

ApplicationApply Model.

Step 9: Connect the out of the retrieve to the tra of the neural net. The mod of neural net is

connected to the mod of apply model.

Step 10: Connect the out of the retrieve to the unl of the apply model. The mod and lab of apply

model is connected to the res of the process.

Step 11: Run the process to view the predicted data and improved neural net.

SCREENSHOTS:

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RESULT:The unknown data are predicted using neural net and apply model of RapidMiner tool.

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IMPLEMENTATION OF NAVE BAYESIAN MODEL ON IMPORTED DATAAIM:

To implement the nave baysian on imported data.

ALGORITHM:STEP 1: Open the rapid miner tool.

STEP 2 : In the process window place the retrieve operator and select the dataset you want to

import.

STEP 3: Place the nave bayes operator and connect it with the retrieve operator.

STEP 4:Place the Apply model operator and connect it with the nave bayes .

STEP 5: Run the process

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SCREEN SHOTS:

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RESULT:Thus the naive baysian has been implemented on the imported data successfully.

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CROSS VALIDATIONAIM:

To perform cross validation in RapidMiner tool using validation operator.

ALGORITHM:Step 1: Open the RapidMiner tool.

Step 2: In the welcome perspective that opens, select the new icon.Step 3: Select the repository location and click ok.

Step 4: Import an excel file and retrieve it.

Step 5: In the design perspective, click operators view Evaluation Validation X-

Validation.

Step 6: Click on the validation process operator which will lead to the training and testing

process.

Step 7: Click operators viewModelingClassification and RegressionBayesian

ModelingNave Bayes and place it in training process.

Step 8: Click ModelingModel ApplicationApply Model and EvaluationPerformance,

MeasurementPerformance and place them in testing process and make connections.

Step 9: Connect the ave of the validation operator to the res of the process.

Step 10: Run the process. The result can be viewed in performance vector view.

SCREENSHOTS:

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RESULT:The cross validation is performed using validation operator of RapidMiner tool.

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CREATION OF GENERIC OPTIMIZATION PREPROCESSORAIM:

To create a generic optimization preprocessor in RapidMiner tool.

ALGORITHM:Step 1: Open the RapidMiner tool.

Step 2: Select a location and name for the local repository.Step 3: Select Operators view Utility Data Generation Generate Data

Step 4: Select Operators view Data TransformationAttribute set Reduction and

Transformation SelectionOptimizationOptimize selection

Step 5: Connect the operators and click on the process which then leads to the validation process.

Step 6: Select operators view EvaluationValidationSplit validation and make the

connections.

Step 7: Click on the process which proceeds to the training and testing process.

Step 8: Select operatorsModelingClassification and Regression Support Vector

Modeling Support Vector Machine in the training phase.

Step 9: Select operators

Modeling

Model Application

Apply Model and Evaluation

Performance and Measurement Performance in the testing phase.

Step 10: Make the appropriate connections.

Step 11: Run the process in forward selection and backward elimination mode.

SCREENSHOTS:

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Forward Selection:

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Backward Elimination:

RESULT:

The forward and backward optimization is created using optimize selection operator of the

RapidMiner tool.

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REPRESENTING DATA USING DECISION TREE

AIM:

To represent data using decision tree in the RapidMiner tool.

ALGORITHM:

Step 1: Open the RapidMiner tool.

Step 2: Select a location and name for the local repository.

Step 3: Select Operators view Utility Data Generation Generate Direct Mailing Data

Step 4: Select operators view EvaluationValidationSplit validation and make the

connections.

Step 5: Open the process which proceeds to the training and testing process.

Step 6: In the training phase, select operatorsModelingClassification and Regression

Tree Induction Decision tree

Step 7: Place the apply model and performance operators in the testing phase.

Step 8: Make the appropriate connections.

Step 9: Run the process to view the performance and decision tree.

SCREENSHOTS:

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RESULT:Thus the data is represented using decision tree in the RapidMiner tool.

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EVOLUTIONARY WEIGHTING OF THE ATTRIBUTES

AIM:

To perform the evolutionary weighting of the attributes using RapidMiner.

ALGORITHM:

Step 1: Open the RapidMiner tool.

Step 2: Select OperatorsModelingAttribute WeightingOptimize Weights (evolutionary)

and place it in the main process.

Step 3: Import and retrieve a dataset whose character field is label.

Step 4: On double clicking, Optimize weights it leads to Evaluation Process. Place Split

Validation operator in it.

Step 5: Again on double clicking the process, leads to Training and Testing phase.

Step 6: In Training phase, place Neuralnet.

Step 7: In testing phase, place ApplyModel and Performance.

Step 8: In the main process, click on the optimize weights operator. The right side of the window

have several options to be modified.

Step 9: Give the population size as 10, check early stopping and show population plotter, set the

selection scheme as roulette wheel, p crossover as 0.2 and crossover type as shuffle.

Step 10: Make the necessary connections and run the process.

RESULT:

Thus the evolutionary weighting of the attributes are obtained using RapidMiner.

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TEXT MINING USING RAPID MINER:

AIM:

To mine the text data using the rapid miner tool.

ALGORITHM:

STEP 1:Open the rapid miner tool.

STEP 2:Place the process document operator .

STEP 3:Select that operator and click Edit List .

STEP 4 :In the dialog box select the text file and click ok

STEP 5:Double Click the operator .The vector process appears.

STEP 6:Place the tokenize operator and set the parameter .Right click and

select set breakpoint.

STEP 7:Place the Filter stopword (English ) and connect it with the tokenize.

STEP 8:Run the process.

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SCREEN SHOTS:

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RESULT:

Thus the text document has been mined by using the rapid miner

successfully.