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Data Mining

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Sonali Parab

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 Insights • text-mining
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MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. A. Report 1 Fig 1.6: Decision Tree
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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)
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 6:Plot iris species in bar plot Fig 1.9: bar plot command C. Report 3 Fig 1.10:Barplot of Species
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 7:Plot iris Species in pie chart Fig 1.11: Command for pie chart D. Report 4 Fig 1.12: Pie Chart
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 2:Filter the data FilterssuperviseddiscretizeApply Fig 2.2 Selecting the Filter Fig 2.3 Structure of Filtered Diabetes.arff Dataset
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 3:Classify the data using Naïve Bayes Algorithm Fig 2.4 Select Classification Algorithm Fig 2.5 Running and Displaying Result
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. === 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. '(-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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. '(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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 4: Visualize classifiers errors Fig 2.6 Visualization of Classification Errors
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Fig 3.2: Filtering the Data Fig 3.3: Filtered Dataset
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. === 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#
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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%)
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 4:Visualizing the Result Right click on resultvisualize cluster assignments Fig 3.6 Selecting Visualization Fig 3.7 Displaying Visualization Result
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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.
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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.
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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.
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Fig 5.2: Filtering the Data Fig 5.3:Filtered Dataset
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. === 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 %
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Fig 6.2: Filtering the Data Fig 6.3: Filtered Dataset
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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) ===
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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)
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ 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)
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Fig 7.4 Inspecting the Results of Apriori Algorithm Fig 7.5 Applying Settings to Display Rules with RHS containing survived only
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 3:Finding and Removing Redundant Rules Code to Find Redundant Rules Code to Remove Redundant Rules Fig 7.6 Finding & Removing Redundant Rules
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Function to plot Association Results as Graph Plot Fig 7.8 Graph Plot Showing How Data Items are Assoicated
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Function to Stem the Document Fig 8.6 Stemming the Document Function to Convert corpusdoc to TermDocumentMatrix Fig 8.7 Inspecting TermDocumentMatrix
MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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”

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Data Mining

  • 1. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 2. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ 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
  • 3. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. A. Report 1 Fig 1.6: Decision Tree
  • 4. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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)
  • 5. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 6:Plot iris species in bar plot Fig 1.9: bar plot command C. Report 3 Fig 1.10:Barplot of Species
  • 6. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 7:Plot iris Species in pie chart Fig 1.11: Command for pie chart D. Report 4 Fig 1.12: Pie Chart
  • 7. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 8. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 9. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 2:Filter the data FilterssuperviseddiscretizeApply Fig 2.2 Selecting the Filter Fig 2.3 Structure of Filtered Diabetes.arff Dataset
  • 10. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 3:Classify the data using Naïve Bayes Algorithm Fig 2.4 Select Classification Algorithm Fig 2.5 Running and Displaying Result
  • 11. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. === 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
  • 12. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. '(-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
  • 13. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. '(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
  • 14. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 4: Visualize classifiers errors Fig 2.6 Visualization of Classification Errors
  • 15. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 16. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Fig 3.2: Filtering the Data Fig 3.3: Filtered Dataset
  • 17. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 18. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. === 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#
  • 19. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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%)
  • 20. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 4:Visualizing the Result Right click on resultvisualize cluster assignments Fig 3.6 Selecting Visualization Fig 3.7 Displaying Visualization Result
  • 21. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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.
  • 22. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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.
  • 23. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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.
  • 24. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 25. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Fig 5.2: Filtering the Data Fig 5.3:Filtered Dataset
  • 26. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 27. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. === 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 %
  • 28. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 29. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 30. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Fig 6.2: Filtering the Data Fig 6.3: Filtered Dataset
  • 31. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 32. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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) ===
  • 33. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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)
  • 34. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ 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)
  • 35. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 36. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 37. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Fig 7.4 Inspecting the Results of Apriori Algorithm Fig 7.5 Applying Settings to Display Rules with RHS containing survived only
  • 38. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Step 3:Finding and Removing Redundant Rules Code to Find Redundant Rules Code to Remove Redundant Rules Fig 7.6 Finding & Removing Redundant Rules
  • 39. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 40. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Function to plot Association Results as Graph Plot Fig 7.8 Graph Plot Showing How Data Items are Assoicated
  • 41. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 42. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 43. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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
  • 44. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. Function to Stem the Document Fig 8.6 Stemming the Document Function to Convert corpusdoc to TermDocumentMatrix Fig 8.7 Inspecting TermDocumentMatrix
  • 45. MSc IT Part – I, Semester-1 Page No:- ________ DATA MINING Date:- ____________ Sonali. Parab. 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”