The document outlines various practical assignments for a Data Mining course using different datasets and tools like R and Weka. Each practical involves building models using algorithms such as decision trees, naive Bayes, k-nearest neighbors, and clustering techniques along with steps for data preprocessing and visualization. Key datasets include iris, diabetes, bodyfat, and contact lenses, with reports generated to analyze results.

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DATA MINING Date:- ____________
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PRACTICAL NO: 1
Aim:Build the data mining model structure and built the decision tree with proper decision
nodes and infer at least five different types of reports. Implement Using RTool.
Solution:
Dataset Used :Iris
Step 1:Display the Structure of iris data.
Fig 1.1: Structure of iris data
Step 2:The random seed is set to a fixed value below to make the results reproducible.
Fig 1.2:Random Seed Set

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Step 3:Install the party package if it is not installed. Load the party package, build adecision
tree, and check the prediction result.
Sonali. Parab.
Fig 1.3: Load Party library
Fig 1.4: iris table
Step 4:printing the rules and plot the tree
Fig 1.5: Rules of data

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A. Report 1
Fig 1.6: Decision Tree

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Step 5:Plot Decision tree in simple style
Fig 1.7: Command to plot decision tree in simple style
B. Report 2
Fig 1.8: Decision tree (Simple Style)

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Step 6:Plot iris species in bar plot
Fig 1.9: bar plot command
C. Report 3
Fig 1.10:Barplot of Species

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Step 7:Plot iris Species in pie chart
Fig 1.11: Command for pie chart
D. Report 4
Fig 1.12: Pie Chart

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Step 8:Plot histogram of iris Petal Length
Fig 1.13: Command to plot histogram
E. Report 5
Fig 1.14: Histogram of iris Petal Length

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PRACTICAL NO: 2
Aim:Build the data mining model structure and Implement Naïve Bayes Algorithm.
Implement Using WEKA.
Solution:
Dataset Used :Diabetes.arff
Step 1:Pre-processing
Go to WekaOpen file go to weka folder select diabetes.arff dataset open
Fig 2.1 Choosing diabetes.arff dataset

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Step 2:Filter the data
FilterssuperviseddiscretizeApply
Fig 2.2 Selecting the Filter
Fig 2.3 Structure of Filtered Diabetes.arff Dataset

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Step 3:Classify the data using Naïve Bayes Algorithm
Fig 2.4 Select Classification Algorithm
Fig 2.5 Running and Displaying Result

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=== Run information ===
Scheme:weka.classifiers.bayes.NaiveBayes
Relation: pima_diabetes-weka.filters.supervised.attribute.Discretize-Rfirst-last
Instances: 768
Attributes: 9
preg
plas
pres
skin
insu
mass
pedi
age
class
Test mode:10-fold cross-validation
=== Classifier model (full training set) ===
Naive Bayes Classifier
Class
Attribute tested_negative tested_positive
(0.65) (0.35)
====================================================
preg

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'(-inf-6.5]' 427.0 174.0
'(6.5-inf)' 75.0 96.0
[total] 502.0 270.0
plas
'(-inf-99.5]' 182.0 17.0
'(99.5-127.5]' 211.0 79.0
'(127.5-154.5]' 86.0 77.0
'(154.5-inf)' 25.0 99.0
[total] 504.0 272.0
pres
'All' 501.0 269.0
[total] 501.0 269.0
skin
'All' 501.0 269.0
[total] 501.0 269.0
insu
'(-inf-14.5]' 237.0 140.0
'(14.5-121]' 165.0 28.0
'(121-inf)' 101.0 103.0
[total] 503.0 271.0
mass
'(-inf-27.85]' 196.0 28.0

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'(27.85-inf)' 306.0 242.0
[total] 502.0 270.0
pedi
'(-inf-0.5275]' 362.0 149.0
'(0.5275-inf)' 140.0 121.0
[total] 502.0 270.0
age
'(-inf-28.5]' 297.0 72.0
'(28.5-inf)' 205.0 198.0
[total] 502.0 270.0
Time taken to build model: 0 seconds

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Step 4: Visualize classifiers errors
Fig 2.6 Visualization of Classification Errors

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PRACTICAL NO: 3
Aim:Implement the clustering Algorithm By Using Weka Tool.
Solution:
Dataset Used :Iris.arff
Step 1:Preprocess
Open file go to weka folder select iris dataset Choose 
Filterssuperviseddiscretize
Fig 3.1: Structure of iris data

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Fig 3.2: Filtering the Data
Fig 3.3: Filtered Dataset

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Step 2:Cluster
Select cluster tabchoose button clusterers  select simplekmeans click
radio button use training setright click “Poperties” numClusters= 3click
start button.
Fig 3.4 Configuring Clustering Algorithm
Fig 3.5 Generating Result

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=== Run information ===
Scheme:weka.clusterers.SimpleKMeans -N 3 -A "weka.core.EuclideanDistance -R first-last"
-I 500 -S 10
Relation: iris-weka.filters.supervised.attribute.Discretize-Rfirst-last
Instances: 150
Attributes: 5
sepallength
sepalwidth
petallength
petalwidth
class
Test mode:evaluate on training data
=== Model and evaluation on training set ===
kMeans
======
Number of iterations: 5
Within cluster sum of squared errors: 109.0
Missing values globally replaced with mean/mode
Cluster centroids:
Cluster#

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Attribute Full Data 0 1 2
(150) (50) (50) (50)
=====================================================
sepallength '(-inf-5.55]' '(-inf-5.55]' '(5.55-6.15]' '(6.15-inf)'
sepalwidth '(-inf-2.95]' '(3.35-inf)' '(-inf-2.95]' '(2.95-3.35]'
petallength '(4.75-inf)' '(-inf-2.45]' '(2.45-4.75]' '(4.75-inf)'
petalwidth '(0.8-1.75]' '(-inf-0.8]' '(0.8-1.75]' '(1.75-inf)'
class Iris-setosa Iris-setosa Iris-versicolor Iris-virginica
Time taken to build model (full training data) : 0 seconds
=== Model and evaluation on training set ===
Clustered Instances
0 50 ( 33%)
1 50 ( 33%)
2 50 ( 33%)

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Step 4:Visualizing the Result
Right click on resultvisualize cluster assignments
Fig 3.6 Selecting Visualization
Fig 3.7 Displaying Visualization Result

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PRACTICAL NO: 4
Aim :Build the basic Time series model structure and create the predictions
BodyFatDataset.By Using RTool.
Solution:
Dataset Used :BodyFat
Step 1 :load Package mboost.
Fig 4.1 : Show the load Of Package mboost.

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Step2 :To Show Data stored in BodyFat Dataset.
Fig 4.2 : Show The Data stored in BodyFat Dataset.
Step 3 :Select the Summary Of BodyFat Dataset.
Fig 4.3 :Show The Summary Of BodyFat Dataset.

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Step4 :Using Predication Method And Plot Graph On BodyFat Dataset.
Fig 4.4 : Show Predication Method And Plot Graph Formula ApplyOn BodyFat Dataset.
Step5 :Predication Graph For BodyFat Dataset.
Fig 4.5 :Show The Predication Graph For BodyFat Dataset.

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PRACTICAL NO: 5
Aim:Build the data mining model and implement k-nearest neighbor By Using Weka Tool.
Solution:
Dataset Used:ContactLenses.arff
Step 1:Preprocess
Open file go to weka folder select contact lens dataset Choose 
Filterssuperviseddiscretize
Fig 5.1: Structure of contact lens dataset

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Fig 5.2: Filtering the Data
Fig 5.3:Filtered Dataset

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Step 2:Classify
Select classify tabchoose buttonexpand Lazy folderselect IBKclick radio button use
training setclick start button.
Fig 5.4 Choosing K-nearest neighbour algorithm
Fig 5.5 Generating Result

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=== Run information ===
Scheme:weka.classifiers.lazy.IBk -K 1 -W 0 -A
"weka.core.neighboursearch.LinearNNSearch -A "weka.core.EuclideanDistance -R first-last""
Relation: contact-lenses-weka.filters.supervised.attribute.Discretize-Rfirst-last
Instances: 24
Attributes: 5
age
spectacle-prescrip
astigmatism
tear-prod-rate
contact-lenses
Test mode:evaluate on training data
=== Classifier model (full training set) ===
IB1 instance-based classifier
using 1 nearest neighbour(s) for classification
Time taken to build model: 0 seconds
=== Evaluation on training set ===
=== Summary ===
Correctly Classified Instances 24 100 %

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Incorrectly Classified Instances 0 0 %
Kappa statistic 1
Mean absolute error 0.0494
Root mean squared error 0.0524
Relative absolute error 13.4078 %
Root relative squared error 12.3482 %
Total Number of Instances 24
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure ROC Area Class
1 0 1 1 1 1 soft
1 0 1 1 1 1 hard
1 0 1 1 1 1 none
Weighted Avg. 1 0 1 1 1 1
=== Confusion Matrix ===
a b c <-- classified as
5 0 0 | a = soft
0 4 0 | b = hard
0 0 15 | c = none

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PRACTICAL NO: 6
Aim:Build the data mining model and implement k-nearest neighbor By Using Weka Tool.
Solution:
Dataset Used:Supermarket.arff
Step 1:Preprocess
Open file go to Weka folder select Supermarket dataset Choose  FiltersAll Filter
Fig 6.1: Structure of Supermarket dataset

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Fig 6.2: Filtering the Data
Fig 6.3: Filtered Dataset

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Step 2:Associate
Select Associate tabchoose apriori algorithmpropertiesconfigure
algorithm according to requirementsclick ‘start’
Fig 6.4 Choosing Apriori Algorithm
Fig 6.5 Configuring Algorithm

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Fig 6.6 Displaying Association Results
=== Run information ===
Scheme: weka.associations.Apriori -N 12 -T 0 -C 0.9 -D 0.05 -U 1.0 -M 0.1 -S -1.0 -c -1
Relation: supermarket-weka.filters.AllFilter-weka.filters.AllFilter-weka.filters.AllFilter-weka.
filters.AllFilter-weka.filters.MultiFilter-Fweka.filters.AllFilter-weka.filters.AllFilter-weka.
filters.AllFilter
Instances: 4627
Attributes: 217
[list of attributes omitted]
=== Associator model (full training set) ===

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Apriori
=======
Minimum support: 0.15 (694 instances)
Minimum metric <confidence>: 0.9
Number of cycles performed: 17
Generated sets of large itemsets:
Size of set of large itemsets L(1): 44
Size of set of large itemsets L(2): 380
Size of set of large itemsets L(3): 910
Size of set of large itemsets L(4): 633
Size of set of large itemsets L(5): 105
Size of set of large itemsets L(6): 1
Best rules found:
1. biscuits=t frozen foods=t fruit=t total=high 788 ==> bread and cake=t 723 conf:(0.92)
2. baking needs=t biscuits=t fruit=t total=high 760 ==> bread and cake=t 696 conf:(0.92)
3. baking needs=t frozen foods=t fruit=t total=high 770 ==> bread and cake=t 705
conf:(0.92)

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4. biscuits=t fruit=t vegetables=t total=high 815 ==> bread and cake=t 746 conf:(0.92)
5. party snack foods=t fruit=t total=high 854 ==> bread and cake=t 779 conf:(0.91)
6. biscuits=t frozen foods=t vegetables=t total=high 797 ==> bread and cake=t 725
conf:(0.91)
7. baking needs=t biscuits=t vegetables=t total=high 772 ==> bread and cake=t 701
conf:(0.91)
Sonali. Parab.
8. biscuits=t fruit=t total=high 954 ==> bread and cake=t 866 conf:(0.91)
9. frozen foods=t fruit=t vegetables=t total=high 834 ==> bread and cake=t 757
conf:(0.91)
10. frozen foods=t fruit=t total=high 969 ==> bread and cake=t 877 conf:(0.91)
11. baking needs=t fruit=t vegetables=t total=high 831 ==> bread and cake=t 752 conf:(0.9)
12. biscuits=t milk-cream=t total=high 907 ==> bread and cake=t 820 conf:(0.9)

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PRACTICAL NO: 7
Aim:Build the data mining model and implement k-nearest neighbor By Using Weka Tool.
Solution:
Dataset Used:Titanic
Step 1:Preprocess
Loading the Data in Data Frame
Transforming the Data into Suitable Format
Fig 7.1: Structure of Titanic dataset

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Fig 7.2 Summary of Titanic Dataset
Step 2:Associate
Loading library ‘arules’ that contains functions for Association mining
Function used to apply Apriori Algorithm with Default Configuration
Fig 7.3 Choosing Apriori Algorithm

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Fig 7.4 Inspecting the Results of Apriori Algorithm
Fig 7.5 Applying Settings to Display Rules with RHS containing survived only

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Step 3:Finding and Removing Redundant Rules
Code to Find Redundant Rules
Code to Remove Redundant Rules
Fig 7.6 Finding & Removing Redundant Rules

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Step 4:Visualizing:
Loading library aulesViz which contains functions for Visualizing Assoication Results
Function to plot Results Using Scatter Plot
X axis: Support
Y axis:Confidence
Fig 7.7 Scatter Plot

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Function to plot Association Results as Graph Plot
Fig 7.8 Graph Plot Showing How Data Items are Assoicated

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PRACTICAL NO: 8
Aim:Consider the suitable data for text mining and Implement the Text Mining technique
using R-Tool.
Solution:
Dataset Used:Plain Text File (www.txt)
Step 1:Loading the Text File
Loading Essential Libraries for Text Mining tm, SnowballC and twitteR
Loading The Data From Text File Into RTool Using readLines()
Fig 8.1: Using tail() and head() functions to display start and of paragraphs

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Step 2:Transforming
Loading tm library and transforming document to Corpusdoc
Fig 8.2 Inspecting Corpusdoc
Function to Remove Punctuations
Fig 8.3 Removing Punctuations

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Function to Strip White Spaces
Fig 8.4 Stripping White Spaces
Function to Remove Stop Words from Document
Fig 8.5 Removing Stop Words From Document

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Function to Stem the Document
Fig 8.6 Stemming the Document
Function to Convert corpusdoc to TermDocumentMatrix
Fig 8.7 Inspecting TermDocumentMatrix

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Step 3:Finding Frequent Terms in Document
Fig 8.7 Find Frequent Terms From Document
Step 4:Finding Association among terms
Function to find Association among Different terms in Document
Fig 8.8 Result of How Strongly Terms Are Associated with Term “information”