GR SOLUTION: 2016

Procedure:

Step1: Open the data file in Weka Explorer. It is presumed that the required data fields have been discretized. In this example it is age attribute.

Step2: Clicking on the associate tab will bring up the interface for association rule algorithm.

Step3: We will use K-means algorithm. This is the default algorithm.

Step4: Inorder to change the parameters for the run (example support, confidence etc) we click on the text box immediately to the right of the choose button.

Scheme: weka.clusterers.SimpleKMeans -init 0 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t1 -1.25 -t2 -1.0 -N 3 -A "weka.core.EuclideanDistance -R first-last" -I 500 -num-slots 1 -S 10

Relation: labor-neg-data

Instances: 57

Attributes: 17

duration

wage-increase-first-year

wage-increase-second-year

wage-increase-third-year

cost-of-living-adjustment

working-hours

pension

standby-pay

shift-differential

education-allowance

statutory-holidays

vacation

longterm-disability-assistance

contribution-to-dental-plan

bereavement-assistance

contribution-to-health-plan

class

Test mode: evaluate on training data

=== Clustering model (full training set) ===

kMeans

======

Number of iterations: 3

Within cluster sum of squared errors: 119.5224194214812

Initial starting points (random):

Cluster 0: 1,5.7,3.971739,3.913333,none,40,empl_contr,7.444444,4,no,11,generous,yes,full,yes,full,good

Cluster 1: 1,2,3.971739,3.913333,tc,40,ret_allw,4,0,no,11,generous,no,none,no,none,bad

Cluster 2: 2,2.5,3,3.913333,tcf,40,none,7.444444,4.870968,no,11,below_average,yes,half,yes,full,bad

Missing values globally replaced with mean/mode

Final cluster centroids:

Cluster#

Attribute Full Data 0 1 2

(57.0) (36.0) (5.0) (16.0)

========================================================================================

duration 2.1607 2.2267 1.4 2.25

wage-increase-first-year 3.8036 4.4695 3.2 2.4938

wage-increase-second-year 3.9717 4.4175 4.183 2.9027

wage-increase-third-year 3.9133 4.1093 3.9133 3.4725

cost-of-living-adjustment none none none none

working-hours 38.0392 37.4766 39.2078 38.94

pension empl_contr empl_contr none empl_contr

standby-pay 7.4444 7.9938 6.7556 6.4236

shift-differential 4.871 5.4776 3.1484 4.0444

education-allowance no no no no

statutory-holidays 11.0943 11.4801 10.6 10.3809

vacation below_average generous below_average below_average

longterm-disability-assistance yes yes no yes

contribution-to-dental-plan half half none half

bereavement-assistance yes yes no yes

contribution-to-health-plan full full none full

class good good bad bad

Time taken to build model (full training data) : 0.01 seconds

=== Model and evaluation on training set ===

Clustered Instances

0 36 ( 63%)

1 5 ( 9%)

2 16 ( 28%)

Scheme: weka.clusterers.SimpleKMeans -init 0 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t1 -1.25 -t2 -1.0 -N 2 -A "weka.core.EuclideanDistance -R first-last" -I 500 -num-slots 1 -S 10

Relation: labor-neg-data

Instances: 57

Attributes: 17

duration

wage-increase-first-year

wage-increase-second-year

wage-increase-third-year

cost-of-living-adjustment

working-hours

pension

standby-pay

shift-differential

education-allowance

statutory-holidays

vacation

longterm-disability-assistance

contribution-to-dental-plan

bereavement-assistance

class

Ignored:

contribution-to-health-plan

Test mode: Classes to clusters evaluation on training data

=== Clustering model (full training set) ===

kMeans

======

Number of iterations: 5

Within cluster sum of squared errors: 122.05464734126849

Initial starting points (random):

Cluster 0: 1,5.7,3.971739,3.913333,none,40,empl_contr,7.444444,4,no,11,generous,yes,full,yes,good

Cluster 1: 1,2,3.971739,3.913333,tc,40,ret_allw,4,0,no,11,generous,no,none,no,bad

Missing values globally replaced with mean/mode

Final cluster centroids:

Cluster#

Attribute Full Data 0 1

(57.0) (43.0) (14.0)

==========================================================================

duration 2.1607 2.213 2

wage-increase-first-year 3.8036 4.2024 2.5786

wage-increase-second-year 3.9717 4.221 3.2062

wage-increase-third-year 3.9133 4.0329 3.5462

cost-of-living-adjustment none none none

working-hours 38.0392 37.6557 39.2171

pension empl_contr empl_contr none

standby-pay 7.4444 7.7778 6.4206

shift-differential 4.871 5.2018 3.8548

education-allowance no no no

statutory-holidays 11.0943 11.2878 10.5

vacation below_average below_average below_average

longterm-disability-assistance yes yes yes

contribution-to-dental-plan half half none

bereavement-assistance yes yes yes

class good good bad

Time taken to build model (full training data) : 0 seconds

=== Model and evaluation on training set ===

Clustered Instances

0 43 ( 75%)

1 14 ( 25%)

Class attribute: contribution-to-health-plan

Classes to Clusters:

0 1 <-- assigned to cluster

20 8 | none

9 0 | half

14 6 | full

Cluster 0 <-- none

Cluster 1 <-- full

Incorrectly clustered instances : 31.0 54.386 %

DATA MINING LABORATORY- IT6711

Hardware Requirements
   RAM Memory -2 GB or more
   Intel Pentium 4 or AMD Athlon 2 GHz (or faster)
   1 GB (or more) available hard disk space

Software Requirements
SQL SERVER 2008,WEKA TOOL,JDK 1.8

EXPERIMENTS:

1. Creation of a Data Warehouse.

2. Apriori Algorithm.

3. FP-Growth Algorithm.

4. K-means clustering.

5. One Hierarchical clustering algorithm.

6. Bayesian Classification.

7. Decision Tree.

8. Support Vector Machines.

9. Applications of classification for web mining.

10. Case Study on Text Mining or any commercial application.

FP-Growth Algorithm implementation in weka tool

Procedure:

Step1: Open the data file in Weka Explorer. It is presumed that the required data fields have been discretized. In this example it is age attribute.

Step2: Clicking on the associate tab will bring up the interface for association rule algorithm.

Step3: We will use FP-Growth algorithm. This is the default algorithm.

Step4: Inorder to change the parameters for the run (example support, confidence etc) we click on the text box immediately to the right of the choose button.

Data set:

Shopping.arff

@relation shopping

@attribute milk{yes,no}

@attribute bread{yes,no}

@attribute honey{yes,no}

@attribute ghee{yes,no}

@attribute jam{yes,no}

@data

yes,yes,no,no,yes

no,yes,no,yes,no

no,yes,yes,no,no

yes,yes,no,yes,no

yes,no,yes,no,no

no,yes,yes,no,no

yes,no,yes,no,no

yes,yes,yes,no,yes

yes,yes,yes,no,no

Apriori Algorithm implementation in weka tool

Procedure:

Step1: Open the data file in Weka Explorer. It is presumed that the required data fields have been discretized. In this example it is age attribute.

Step2: Clicking on the associate tab will bring up the interface for association rule algorithm.

Step3: We will use apriori algorithm. This is the default algorithm.

Step4: Inorder to change the parameters for the run (example support, confidence etc) we click on the text box immediately to the right of the choose button.

Data set:

Shopping.arff

@relation shopping

@attribute milk{yes,no}

@attribute bread{yes,no}

@attribute honey{yes,no}

@attribute ghee{yes,no}

@attribute jam{yes,no}

@data

yes,yes,no,no,yes

no,yes,no,yes,no

no,yes,yes,no,no

yes,yes,no,yes,no

yes,no,yes,no,no

no,yes,yes,no,no

yes,no,yes,no,no

yes,yes,yes,no,yes

yes,yes,yes,no,no

MOBILE APPLICATION DEVELOPMENT LAB FOR JNTU SYLLUBUS

Lab manual features
*A complete Lab Manual with pdf,source code,Procedure,download project and download android apk file for all experiment.
*Step by step procedure to install and execute all the programs.
*JNTU Latest Sylabus.Common to CSE ,MCA and IT department
*Easy to execute all the experiment simply download the project.
*Have a android executable(.apk) file.Download and Run in your android device.
*Step by step procedure is available with the manual.So that easily Run the experiment without the help of other.
A complete Lab Manual with source code for all experiment.

One day  Workshop:
  Workshop on Mobile application development is available for colleges.Content for the workshop are as follows
  *Recent trends in mobile application development
*Introduction about mobile application development environment(Android).
*Life cycle of android .Working nature of android software.
*Step by step installation for android software.
*Execution of all lab experiments.
Contact:8344790950

LabManual cost details
LAB MANUAL DVD(Rs 1000)
The labmanual with dvd it includes all experiment in pdf,software and installation procedure.The labmanul cost RS1000.The dvd will be reach you with in one or two days.
LAB MANUAL SOFT COPY(Rs 700)
The softcopy of lab manual is availabe via email.It includes all experiment in pdf,installation procedure.The labmanual cost RS700.The email will reach you shortly after payment.

Payment method:
The payment has be made to the following bank account.After payment contact as we will send our lab manual.

contact details:

CELL:8344790950

EMAIL:hitechguil@gmail.com

Bank Account Details:

BANK NAME :STATE BANK OF INDIA

ACCOUNT NO:20222847933

ACCOUNT HOLDER NAME:P GUILBERT RAJ

IFSC CODE:SBIN0010501