Monroe College Data Mining Lab 1 Report The objective of the lab is to become familiar with WEKA software. Follow instruction and your final results should

Monroe College Data Mining Lab 1 Report The objective of the lab is to become familiar with WEKA software. Follow instruction and your final results should be uploaded using this dropbox. Make sure you create a document with your results.Please refer the attachment. Lab 1-2
Objective:
Create an Employee Table with the help of Data Mining Tool WEKA.
Description:
We need to create an Employee Table with training data set which includes attributes like name, id, salary,
experience, gender, phone number.
Procedure:
Steps:
1) Open Start  Programs  Accessories  Notepad
2) Type the following training data set with the help of Notepad for Employee Table.
@relation employee
@attribute name {x,y,z,a,b}
@attribute id numeric
@attribute salary {low,medium,high}
@attribute exp numeric
@attribute gender {male,female}
@attribute phone numeric
@data
x,101,low,2,male,250311
y,102,high,3,female,251665
z,103,medium,1,male,240238
a,104,low,5,female,200200
b,105,high,2,male,240240
3) After that the file is saved with .arff file format.
4) Minimize the arff file and then open Start  Programs  weka-3-4.
5) Click on weka-3-4, then Weka dialog box is displayed on the screen.
6) In that dialog box there are four modes, click on explorer.
7) Explorer shows many options. In that click on ‘open file’ and select the arff file
8) Click on edit button which shows employee table on weka.
2
Training Data Set  Weather Table
Result:
Create a file with your results and make sure you know the steps to create a data file.
3
Lab 1-3
Objective:
Apply Pre-Processing techniques to the training data set of Weather Table
Description:
Real world databases are highly influenced to noise, missing and inconsistency due to their queue size so the
data can be pre-processed to improve the quality of data and missing results and it also improves the efficiency.
There are 3 pre-processing techniques they are:
1) Add
2) Remove
3) Normalization
Creation of Weather Table:
Procedure:
1) Open Start  Programs  Accessories  Notepad
2) Type the following training data set with the help of Notepad for Weather Table.
@relation weather
@attribute outlook {sunny,rainy,overcast}
@attribute temparature numeric
@attribute humidity numeric
@attribute windy {true,false}
@attribute play {yes,no}
3)
4)
5)
6)
7)
8)
@data
sunny,85.0,85.0,false,no
overcast,80.0,90.0,true,no
sunny,83.0,86.0,false,yes
rainy,70.0,86.0,false,yes
rainy,68.0,80.0,false,yes
rainy,65.0,70.0,true,no
overcast,64.0,65.0,false,yes
sunny,72.0,95.0,true,no
sunny,69.0,70.0,false,yes
rainy,75.0,80.0,false,yes
After that the file is saved with .arff file format.
Minimize the arff file and then open Start  Programs  weka-3-4.
Click on weka-3-4, then Weka dialog box is displayed on the screen.
In that dialog box there are four modes, click on explorer.
Explorer shows many options. In that click on ‘open file’ and select the arff file
Click on edit button which shows weather table on weka.
6
Add  Pre-Processing Technique:
Procedure:
1) Start  Programs  Weka-3-4  Weka-3-4
2) Click on explorer.
3) Click on open file.
4) Select Weather.arff file and click on open.
5) Click on Choose button and select the Filters option.
6) In Filters, we have Supervised and Unsupervised data.
7) Click on Unsupervised data.
8) Select the attribute Add.
9) A new window is opened.
10) In that we enter attribute index, type, data format, nominal label values for Climate.
11) Click on OK.
12) Press the Apply button, then a new attribute is added to the Weather Table.
13) Save the file.
14) Click on the Edit button, it shows a new Weather Table on Weka.
Weather Table after adding new attribute CLIMATE:
7
Remove  Pre-Processing Technique:
Procedure:
1) Start  Programs  Weka-3-4  Weka-3-4
2) Click on explorer.
3) Click on open file.
4) Select Weather.arff file and click on open.
5) Click on Choose button and select the Filters option.
6) In Filters, we have Supervised and Unsupervised data.
7) Click on Unsupervised data.
8) Select the attribute Remove.
9) Select the attributes windy, play to Remove.
10) Click Remove button and then Save.
11) Click on the Edit button, it shows a new Weather Table on Weka.
8
Weather Table after removing attributes WINDY, PLAY:
Normalize  Pre-Processing Technique:
Procedure:
1) Start  Programs  Weka-3-4  Weka-3-4
2) Click on explorer.
3) Click on open file.
4) Select Weather.arff file and click on open.
5) Click on Choose button and select the Filters option.
6) In Filters, we have Supervised and Unsupervised data.
7) Click on Unsupervised data.
8) Select the attribute Normalize.
9) Select the attributes temparature, humidity to Normalize.
10) Click on Apply button and then Save.
11) Click on the Edit button, it shows a new Weather Table with normalized values on Weka.
Weather Table after Normalizing TEMPARATURE, HUMIDITY:
9
Result:
Copy your resuklts to a document file.
10
Lab 1-4
Objective:
Apply Pre-Processing techniques to the training data set of Employee Table
Description:
Real world databases are highly influenced to noise, missing and inconsistency due to their queue size so the
data can be pre-processed to improve the quality of data and missing results and it also improves the efficiency.
There are 3 pre-processing techniques they are:
1) Add
2) Remove
3) Normalization
Creation of Employee Table:
Procedure:
1) Open Start  Programs  Accessories  Notepad
2) Type the following training data set with the help of Notepad for Employee Table.
@relation employee
@attribute name {x,y,z,a,b}
@attribute id numeric
@attribute salary {low,medium,high}
@attribute exp numeric
@attribute gender {male,female}
@attribute phone numeric
@data
x,101,low,2,male,250311
y,102,high,3,female,251665
z,103,medium,1,male,240238
a,104,low,5,female,200200
b,105,high,2,male,240240
3)
4)
5)
6)
7)
8)
After that the file is saved with .arff file format.
Minimize the arff file and then open Start  Programs  weka-3-4.
Click on weka-3-4, then Weka dialog box is displayed on the screen.
In that dialog box there are four modes, click on explorer.
Explorer shows many options. In that click on ‘open file’ and select the arff file
Click on edit button which shows employee table on weka.
11
Training Data Set  Employee Table
Add  Pre-Processing Technique:
Procedure:
1) Start  Programs  Weka-3-4  Weka-3-4
2) Click on explorer.
3) Click on open file.
4) Select Employee.arff file and click on open.
5) Click on Choose button and select the Filters option.
6) In Filters, we have Supervised and Unsupervised data.
7) Click on Unsupervised data.
8) Select the attribute Add.
9) A new window is opened.
10) In that we enter attribute index, type, data format, nominal label values for Address.
11) Click on OK.
12) Press the Apply button, then a new attribute is added to the Employee Table.
13) Save the file.
14) Click on the Edit button, it shows a new Employee Table on Weka.
12
Employee Table after adding new attribute ADDRESS:
Remove  Pre-Processing Technique:
Procedure:
1) Start  Programs  Weka-3-4  Weka-3-4
2) Click on explorer.
3) Click on open file.
4) Select Employee.arff file and click on open.
5) Click on Choose button and select the Filters option.
6) In Filters, we have Supervised and Unsupervised data.
7) Click on Unsupervised data.
8) Select the attribute Remove.
9) Select the attributes salary, gender to Remove.
10) Click Remove button and then Save.
11) Click on the Edit button, it shows a new Employee Table on Weka.
13
Employee Table after removing attributes SALARY, GENDER:
Normalize  Pre-Processing Technique:
Procedure:
1) Start  Programs  Weka-3-4  Weka-3-4
2) Click on explorer.
3) Click on open file.
4) Select Employee.arff file and click on open.
5) Click on Choose button and select the Filters option.
6) In Filters, we have Supervised and Unsupervised data.
7) Click on Unsupervised data.
8) Select the attribute Normalize.
9) Select the attributes id, experience, phone to Normalize.
10) Click on Apply button and then Save.
11) Click on the Edit button, it shows a new Employee Table with normalized values on Weka.
14
Employee Table after Normalizing ID, EXP, PHONE:
Result:
Save your results to a document.
15
Lab 1-5
Objective:
Normalize Weather Table data using Knowledge Flow.
Description:
The knowledge flow provides an alternative way to the explorer as a graphical front end to WEKA’s
algorithm. Knowledge flow is a working progress. So, some of the functionality from explorer is not yet available. So,
on the other hand there are the things that can be done in knowledge flow, but not in explorer. Knowledge flow
presents a dataflow interface to WEKA. The user can select WEKA components from a toolbar placed them on a
layout campus and connect them together in order to form a knowledge flow for processing and analyzing the data.
Creation of Weather Table:
Procedure:
1) Open Start  Programs  Accessories  Notepad
2) Type the following training data set with the help of Notepad for Weather Table.
@relation weather
@attribute outlook {sunny,rainy,overcast}
@attribute temparature numeric
@attribute humidity numeric
@attribute windy {true,false}
@attribute play {yes,no}
3)
4)
5)
6)
7)
8)
@data
sunny,85.0,85.0,false,no
overcast,80.0,90.0,true,no
sunny,83.0,86.0,false,yes
rainy,70.0,86.0,false,yes
rainy,68.0,80.0,false,yes
rainy,65.0,70.0,true,no
overcast,64.0,65.0,false,yes
sunny,72.0,95.0,true,no
sunny,69.0,70.0,false,yes
rainy,75.0,80.0,false,yes
After that the file is saved with .arff file format.
Minimize the arff file and then open Start  Programs  weka-3-4.
Click on weka-3-4, then Weka dialog box is displayed on the screen.
In that dialog box there are four modes, click on explorer.
Explorer shows many options. In that click on ‘open file’ and select the arff file
Click on edit button which shows Weather table on weka.
16
Output:
Training Data Set  Weather Table
Procedure for Knowledge Flow:
1) Open Start  Programs  Weka-3-4  Weka-3-4
2) Open the Knowledge Flow.
3) Select the Data Source component and add Arff Loader into the knowledge layout canvas.
4) Select the Filters component and add Attribute Selection and Normalize into the knowledge layout canvas.
5) Select the Data Sinks component and add Arff Saver into the knowledge layout canvas.
6) Right click on Arff Loader and select Configure option then the new window will be opened and select
Weather.arff
7) Right click on Arff Loader and select Dataset option then establish a link between Arff Loader and
Attribute Selection.
8) Right click on Attribute Selection and select Dataset option then establish a link between Attribute
Selection and Normalize.
9) Right click on Attribute Selection and select Configure option and choose the best attribute for Weather
data.
10) Right click on Normalize and select Dataset option then establish a link between Normalize and Arff Saver.
11) Right click on Arff Saver and select Configure option then new window will be opened and set the path,
enter .arff in look in dialog box to save normalize data.
12) Right click on Arff Loader and click on Start Loading option then everything will be executed one by one.
13) Check whether output is created or not by selecting the preferred path.
14) Rename the data name as a.arff
15) Double click on a.arff then automatically the output will be opened in MS-Excel.
17
Result:
Create a dcument with your results and upload it in blackboard.
18
EXPERIMENT NO:6
Aim:
Normalize Employee Table data using Knowledge Flow.
Description:
The knowledge flow provides an alternative way to the explorer as a graphical front end to WEKA’s
algorithm. Knowledge flow is a working progress. So, some of the functionality from explorer is not yet available. So,
on the other hand there are the things that can be done in knowledge flow, but not in explorer. Knowledge flow
presents a dataflow interface to WEKA. The user can select WEKA components from a toolbar placed them on a
layout campus and connect them together in order to form a knowledge flow for processing and analyzing the data.
Creation of Employee Table:
Procedure:
1) Open Start  Programs  Accessories  Notepad
2) Type the following training data set with the help of Notepad for Employee Table.
@relation employee
@attribute eid numeric
@attribute ename {raj,ramu,anil,sunil,rajiv,sunitha,kavitha,suresh,ravi,ramana,ram,kavya,navya}
@attribute salary numeric
@attribute exp numeric
@attribute address {pdtr,kdp,nlr,gtr}
@data
101,raj,10000,4,pdtr
102,ramu,15000,5,pdtr
103,anil,12000,3,kdp
104,sunil,13000,3,kdp
105,rajiv,16000,6,kdp
106,sunitha,15000,5,nlr
107,kavitha,12000,3,nlr
108,suresh,11000,5,gtr
109,ravi,12000,3,gtr
110,ramana,11000,5,gtr
111,ram,12000,3,kdp
112,kavya,13000,4,kdp
113,navya,14000,5,kdp
3) After that the file is saved with .arff file format.
4) Minimize the arff file and then open Start  Programs  weka-3-4.
5) Click on weka-3-4, then Weka dialog box is displayed on the screen.
6) In that dialog box there are four modes, click on explorer.
7) Explorer shows many options. In that click on ‘open file’ and select the arff file
8) Click on edit button which shows employee table on weka.
19
Output:
Training Data Set  Employee Table
Procedure for Knowledge Flow:
1) Open Start  Programs  Weka-3-4  Weka-3-4
2) Open the Knowledge Flow.
3) Select the Data Source component and add Arff Loader into the knowledge layout canvas.
4) Select the Filters component and add Attribute Selection and Normalize into the knowledge layout canvas.
5) Select the Data Sinks component and add Arff Saver into the knowledge layout canvas.
6) Right click on Arff Loader and select Configure option then the new window will be opened and select
Employee.arff
7) Right click on Arff Loader and select Dataset option then establish a link between Arff Loader and
Attribute Selection.
8) Right click on Attribute Selection and select Dataset option then establish a link between Attribute
Selection and Normalize.
9) Right click on Attribute Selection and select Configure option and choose the best attribute for Employee
data.
10) Right click on Normalize and select Dataset option then establish a link between Normalize and Arff Saver.
11) Right click on Arff Saver and select Configure option then new window will be opened and set the path,
enter .arff in look in dialog box to save normalize data.
12) Right click on Arff Loader and click on Start Loading option then everything will be executed one by one.
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13) Check whether output is created or not by selecting the preferred path.
14) Rename the data name as a.arff
15) Double click on a.arff then automatically the output will be opened in MS-Excel.
Result:
This program has been successfully executed.
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