Data mining (DM) manual. Data mining refers to the process of analysing the data from different perspectives and summarizing it into useful information. Data mining software is one of the number of tools used for analysing data. It allows users to analyse from many different dimensions and angles, categorize it, and summarize the relationship identified. Data mining is about technique for finding and describing Structural Patterns in data.

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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”