DM + KAVITHA

download DM + KAVITHA

of 22

  • date post

    23-Nov-2014
  • Category

    Documents

  • view

    121
  • download

    2

Embed Size (px)

Transcript of DM + KAVITHA

SHEET NO______

INPUTdecisiontree.csv file

decisiontree.arff file

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

OUTPUT1)Open decision.arff file in weka software

2)Choose Classify

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

2)Choose ID3 Tree

3)Run the decisiontree.arff file

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

RESULTClassifier Output :=== Run information === Scheme: weka.classifiers.trees.Id3 Relation: lokesh.symbolic Instances: 14 Attributes: 5 age income student credit_rating buys_computer Test mode: 10-fold cross-validation === Classifier model (full training set) === Id3 age = 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 0.6889 Mean absolute error 0.1429 Root 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 no Weighted Avg. 0.857 0.168 0.857 0.857 0.857 0.844 === Confusion Matrix === a b 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)

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

INPUTKmeans.csv file

kmeans.arff file

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

OUTPUT1)Open the kmeans.arff file in weka software

2)Choose cluster

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

3)Choose SimpleKmeans

4)Set numClusters and choose Manhattan Distance

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

5)Run the kmeans.arff file

6)Choose Visualize Cluster Assignments

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

7)Clusterer Visualize

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

RESULTClusterer Output :=== Run information === Scheme: -S 10 Relation: Instances: Attributes: X Y Test mode: weka.clusterers.SimpleKMeans -N 3 -A "weka.core.ManhattanDistance -R first-last" -I 500 saikumar 8 2 evaluate on training data

=== Model and evaluation on training set === kMeans ====== Number of iterations: 3 Sum of within cluster distances: 1.6071428571428572 Missing 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 4 Y 5 4 3.5 9 Clustered Instances 0 1 2 3 ( 38%) 2 ( 25%) 3 ( 38%)

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

INPUTDbscan.csv file

dbscan.arff file

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

OUTPUT1)Open dbscan.arff file in weka software

2)Choose Cluster

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

3)Choose DBSCAN

4)Set Epsilon and Minpoints

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

5)Run the file

6)Choose Visualize Cluster Assignments

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

7)Clusterer Visualize

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY

SHEET NO______

RESULTClusterer Output:=== Run information === Scheme: weka.clusterers.DBScan -E 0.4 -M 3 -I weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase -D weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject Relation: saikumar Instances: 8 Attributes: 2 X Y Test mode: evaluate on training data === Model and evaluation on training set === DBScan clustering results ================================================================== ====================== Clustered DataObjects: 8 Number of attributes: 2 Epsilon: 0.4; minPoints: 3 Index: weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase Distance-type: weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclidianDataObject Number of generated clusters: 2 Elapsed time: .01 (0.) 2,10 (1.) 2,5 (2.) 8,4 (3.) 5,8 (4.) 7,5 (5.) 6,4 (6.) 1,2 (7.) 4,9 Clustered Instances 0 1 3 ( 50%) 3 ( 50%) --> 1 --> NOISE --> 0 --> 1 --> 0 --> 0 --> NOISE --> 1

Unclustered instances : 2

SRI KAVITHA ENGINEERING COLLEGE,KAREPALLY