Download - DM + KAVITHA

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INPUT

decisiontree.csv file

decisiontree.arff file

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

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OUTPUT

1)Open decision.arff file in weka software

2)Choose Classify


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2)Choose ID3 Tree

3)Run the decisiontree.arff file


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RESULT

Classifier Output :=== Run information ===Scheme: weka.classifiers.trees.Id3 Relation: lokesh.symbolicInstances: 14Attributes: 5 age income student credit_rating buys_computerTest mode: 10-fold cross-validation=== Classifier model (full training set) ===Id3age = <=30| student = no: no| student = yes: yesage = 31..40: yesage = >40| credit_rating = fair: yes| credit_rating = excellent: no

Time taken to build model: 0 seconds=== Stratified cross-validation ====== Summary ===

Correctly Classified Instances 12 85.7143 %Incorrectly Classified Instances 2 14.2857 %Kappa statistic 0.6889Mean absolute error 0.1429Root mean squared error 0.378 Relative absolute error 30 %Root relative squared error 76.6097 %Total Number of Instances 14

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.889 0.2 0.889 0.889 0.889 0.844 yes 0.8 0.111 0.8 0.8 0.8 0.844 noWeighted Avg. 0.857 0.168 0.857 0.857 0.857 0.844

=== Confusion Matrix ===

a b <-- classified as 8 1 | a = yes 1 4 | b = no


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4)Choose Visualize Tree


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RESULT

Classifier Output :=== Run information ===Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2Relation: lokesh.symbolicInstances: 14Attributes: 5 age income student credit_rating buys_computerTest mode: 10-fold cross-validation=== Classifier model (full training set) ===J48 pruned tree------------------age = <=30| student = no: no (3.0)| student = yes: yes (2.0)age = 31..40: yes (4.0)age = >40| credit_rating = fair: yes (3.0)| credit_rating = excellent: no (2.0)

Number of Leaves : 5Size of the tree : 8Time taken to build model: 0.08 seconds=== Stratified cross-validation ====== Summary ===Correctly Classified Instances 7 50 %Incorrectly Classified Instances 7 50 %Kappa statistic -0.0426Mean absolute error 0.4167Root mean squared error 0.5984Relative absolute error 87.5 %Root relative squared error 121.2987 %Total Number of Instances 14 === Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.556 0.6 0.625 0.556 0.588 0.633 yes 0.4 0.444 0.333 0.4 0.364 0.633 noWeighted Avg. 0.5 0.544 0.521 0.5 0.508 0.633

=== Confusion Matrix === a b <-- classified as 5 4 | a = yes 3 2 | b = no


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INPUT

Apriori.csv file

apriori.arff file


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OUTPUT

1)Open apriori.arff file in weka software

2)Choose Associate


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3)Set minimum support and minimum confidence values

4)Run the apriori.arff file


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Result

Associator Output :

=== Run information ===

Scheme: weka.associations.Apriori -N 10 -T 0 -C 0.7 -D 0.05 -U 1.0 -M 0.4 -S -1.0 -c -1Relation: aprioriInstances: 5Attributes: 6 A B C D E K=== Associator model (full training set) ===

Apriori=======

Minimum support: 0.7 (3 instances)Minimum metric <confidence>: 0.7Number of cycles performed: 6

Generated sets of large itemsets:

Size of set of large itemsets L(1): 6



Best rules found:

1. B=TRUE 4 ==> A=TRUE 4 conf:(1) 2. D=TRUE 4 ==> A=TRUE 4 conf:(1) 3. C=TRUE 3 ==> A=TRUE 3 conf:(1) 4. E=FALSE 3 ==> A=TRUE 3 conf:(1) 5. K=FALSE 3 ==> A=TRUE 3 conf:(1) 6. K=FALSE 3 ==> B=TRUE 3 conf:(1) 7. E=FALSE 3 ==> D=TRUE 3 conf:(1) 8. B=TRUE D=TRUE 3 ==> A=TRUE 3 conf:(1) 9. B=TRUE K=FALSE 3 ==> A=TRUE 3 conf:(1)10. A=TRUE K=FALSE 3 ==> B=TRUE 3 conf:(1)


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INPUT

Kmeans.csv file

kmeans.arff file


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OUTPUT

1)Open the kmeans.arff file in weka software

2)Choose cluster


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3)Choose SimpleKmeans

4)Set numClusters and choose Manhattan Distance


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5)Run the kmeans.arff file

6)Choose Visualize Cluster Assignments


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7)Clusterer Visualize


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RESULT

Clusterer Output :


Scheme: weka.clusterers.SimpleKMeans -N 3 -A "weka.core.ManhattanDistance -R first-last" -I 500 -S 10Relation: saikumarInstances: 8Attributes: 2 X YTest mode: evaluate on training data

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

kMeans======

Number of iterations: 3Sum of within cluster distances: 1.6071428571428572Missing values globally replaced with mean/mode

Cluster centroids: Cluster#Attribute Full Data 0 1 2 (8) (3) (2) (3)=======================================================X 4.5 7 1.5 4Y 5 4 3.5 9

Clustered Instances

0 3 ( 38%)1 2 ( 25%)2 3 ( 38%)


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INPUT

Dbscan.csv file

dbscan.arff file


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OUTPUT

1)Open dbscan.arff file in weka software

2)Choose Cluster


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3)Choose DBSCAN

4)Set Epsilon and Minpoints


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5)Run the file

6)Choose Visualize Cluster Assignments


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7)Clusterer Visualize


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RESULT

Clusterer Output:


Scheme: weka.clusterers.DBScan -E 0.4 -M 3 -I weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase -D weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObjectRelation: saikumarInstances: 8Attributes: 2 X YTest mode: evaluate on training data

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

DBScan clustering results========================================================================================

Clustered DataObjects: 8Number of attributes: 2Epsilon: 0.4; minPoints: 3Index: weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabaseDistance-type: weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObjectNumber of generated clusters: 2Elapsed time: .01

(0.) 2,10 --> 1(1.) 2,5 --> NOISE(2.) 8,4 --> 0(3.) 5,8 --> 1(4.) 7,5 --> 0(5.) 6,4 --> 0(6.) 1,2 --> NOISE(7.) 4,9 --> 1

Clustered Instances

0 3 ( 50%)1 3 ( 50%)

Unclustered instances : 2
