Jhansi Rani Kunaparaju

 6. Demonstrate knowledge flow application on data sets age specticle_prescrip astigmation tear_prod_rate contact_lenses young myopia no reduce none young myopia no normal soft young myopia yes reduce none young myopia yes normal hard young hypermertropia no reduce none young hypermertropia no normal soft young hypermertropia yes reduce none young hypermertropia yes normal hard pre_pres_byopic myopia no reduce none pre_pres_byopic myopia no normal none pre_pres_byopic myopia yes reduce soft pre_pres_byopic myopia yes normal hard pre_pres_byopic hypermertropia yes normal none pre_pres_byopic hypermertropia no reduce normal pre_pres_byopic hypermertropia yes reduce normal pre_pres_byopic myopia no normal none pre_pres_byopic myopia yes reduce none pres_byopic hypermertropia yes normal soft 1.Develop a knowledge flow layout for finding strong association rules by using, Apriori algorithm Aim: Demonstration of association rule process on contactlens.arff using apriori algorithm. ...

  Aim: Demonstration of clustering rule process on iris.arff using simple k means algorithm. The experiment illustrates the use of simple k-means clustering weka explorer. The sample data set use for this experiment is iris data in arff format. Steps involved in experiment: Step 1: create iris.arff file @relation iris @attribute sepallenght numeric @attribute sepalwidth numeric @attribute petallenght numeric @attribute petalwidth numeric @attribute class{iris-setosa,iris-veriscolor} @data 5.1,3.5,1.4,0.2,iris-setosa 4.9,3.0,1.4,0.2,iris-setosa 4.3,3.0,1.1,0.1,iris-setosa 7.0,3.2,4.7,1.4,iris-veriscolor 6.4,3.2,4.5,1.5, iris-veriscolor 6.9,3.1,4.9,1.5 ,iris-veriscolor 6.3,3.3,6.0,1.9 ,iris-veriscolor 5.8,2.7,5.1,2.1, ,iris-veriscolor 7.1,3.0,5.9,1.8, ,iris-veriscolor Step2: From the weka explorer load the data file iris .arff into weka  Step 3: in order to perform clustering select the cluster tab and click on the choose button. This steps results in dropdown list of available ...

 Exp 4 Steps involved in experiment: Step 1: create Employee.arff file @relation employee @attribute age {25,27,28,29,30,35,48} @attribute salary {10k,15k,17k,20k,25k,30k,32k} @attribute performance {poor,average,good} @data 25,10k,poor 27,15k,poor 27,17k,poor 28,17k,poor 29,20k,average 30,25k,average 29,25k,average 30,20k,average 35,32k,good 35,30k,good 48,32k,good Strep 2:we  begin experiment by loading the data step3: next to you select classifier tab and click choose button to select j48 classifier. Step 4: now we specify the various parameters this can done by right click on the text box on right of the choose button. Step 5: Now wake also let us view of graphical version of the classification tree. this can be done by right clicking the last result set and selecting visualized tree from popup menu. Output: //// note down from your system////😀