EXPERIMENT 1: CREATE STUDENT DETAILS ARFF FILE AND ANALIZE ATTRIBUTE STEP 1: OPEN NOTEPAD WITH NAME “STUDENT_DETAILS.ARFF” STEP 2: FILE CONTENT: @RELATION student_details @ATTRIBUTE SID STRING @ATTRIBUTE SNAME STRING @ATTRIBUTE SMARKS NUMERIC @ATTRIBUTE GENDER {M,F} @ATTRIBUTE AGE NUMERIC @ATTRIBUTE BRANCH {IT,CSE,EEE,ECE} @DATA S1,AAA,34,M,19,EEE S2,SSS,90,M,20,IT S3,AAA,34,F,19,ECE S4,SSR,56,M,20,IT S5,TVS,34,M,19,EEE S6,TYI,90,F,20,IT S7,HJK,34,M,19,ECE S8,DFG,90,M,20,IT S9,ASD,34,F,19,CSE S10,SSS,90,M,20,IT
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EXPERIMENT 1: CREATE STUDENT DETAILS ARFF FILE AND ANALIZE ATTRIBUTE
STEP 1: OPEN NOTEPAD WITH NAME “STUDENT_DETAILS.ARFF”
STEP 2: FILE CONTENT:
@RELATION student_details
@ATTRIBUTE SID STRING
@ATTRIBUTE SNAME STRING
@ATTRIBUTE SMARKS NUMERIC
@ATTRIBUTE GENDER {M,F}
@ATTRIBUTE AGE NUMERIC
@ATTRIBUTE BRANCH {IT,CSE,EEE,ECE}
@DATA
S1,AAA,34,M,19,EEE
S2,SSS,90,M,20,IT
S3,AAA,34,F,19,ECE
S4,SSR,56,M,20,IT
S5,TVS,34,M,19,EEE
S6,TYI,90,F,20,IT
S7,HJK,34,M,19,ECE
S8,DFG,90,M,20,IT
S9,ASD,34,F,19,CSE
S10,SSS,90,M,20,IT
STEP3: START WEKA
STEP4: SELECT EXPLORER IN “WEKA GUI CHOOSER”
STEP 5: OPEN INPUT FILE WITH NAME “STUDENT_DETAILS.ARFF”
STEP6: OUTPUT
EXPERIMENT 2: DATA PREPROSESSING USING DISCRETIZATION ON STUDENT DETAILS DATA
STEP1: OPEN INPUT FILE NAME “STUDENT_DETAILS.ARFF” UNDER “PREPROCESS” TAB OF WEKA EXPLORER
STEP 2: CHOOSE DATA PREPROCESS FILTER “DISCRETIZE” UNDER “PREPROCESS” TAB
STEP3: OUTPUT (ATTRIBUTE “SMARKS” IS DISCRETIZED INTO TWO RANGES)
EXPERIMENT 3: GENERATE STRONG/BEST ASSOCIATION RULES FOR ALL ELECTRONICS SALES DATA USING APRIORI ALGORITHM
STEP 1: OPEN “SALES.ARFF”FILE UNDER “PREPROCESS” TAB IN WEKA EXPLORER.
STEP 2: CHOOSE “ASSOCIATE” TAB IN WEKA EXPLORER
STEP 3: CHOOSE “APRIORI” ASSOCIATION ANALYSIS ALGORITHM AND CONFIGURE THE ALGORITHM BY SETTING MIN_SUP & MIN_CONFIDANCE VALUES
STEP 4: CLICK “START”BUTTON IN “ASSOCIATE” TAB TO GET BEST ASSOCIATION RULES
EXPERIMENT4: GENERATE STRONG/BEST ASSOCIATION RULES FOR ALL ELECTRONICS SALES DATA USING FREQUENT PATTERN GROWTH ALGORITHM
STEP 1: OPEN “SALES.ARFF”FILE UNDER “PREPROCESS” TAB IN “WEKA EXPLORER”
STEP 2: CHOOSE “ASSOCIATE” TAB IN “WEKA EXPLORER”
STEP 3: CHOOSE FP-GROWTH ASSOCIATION ANALYSIS ALGORITHM AND CONFIGURE THE ALGORITHM BY SETTING MIN_SUP & MIN_CONF
STEP 4: CLICK “START” BUTTON UNDER ASSOCIATE TAB TO GET THE BEST ASSOCIATION RULES
EXPERIMENT5: CLASSIFICATION BY DECISION TREE INDUCTION FOR ANALYSING WEATHER DATA TO DECIDE TO PLAY OR NOT
STEP 1: OPEN INPUT FILE”WEATHER.ARFF” UNDER PREPROCESS TAB IN WEKA EXPLORER
STEP2: SELECT “CLASSIFY” TAB IN “WEKA EXPLORER”
STEP3: CHOOSE “J48” DECISION TREE CLASSIFIER UNDER “TREES” IN “CLASSIFY” TAB.
STEP 4: CLICK “START” BUTTON UNDER “CLASSIFY” TAB TO GET THE BEST CLASSIFICATION RULES AND RIGHT CLICK ON “RESULT LIST” TO VISUALIZE THE TREE.
EXPERIMENT 6: CLASSIFICATION BY BAYES FOR ANALYSING WEATHER DATA TO DECIDE TO PLAY OR NOT
STEP 1: OPEN INPUT FILE”WEATHER.ARFF” UNDER PREPROCESS TAB IN WEKA EXPLORER
STEP2: SELECT “CLASSIFY” TAB IN “WEKA EXPLORER”
STEP3: CHOOSE “NAÏVE BAYES” CLASSIFIER UNDER “BAYES” IN “CLASSIFY” TAB.
STEP 4: CLICK “START” BUTTON UNDER “CLASSIFY” TAB TO GET THE BEST CLASSIFICATION RULES AND RIGHT CLICK ON “RESULT LIST” TO VISUALIZE THE CURVE.
EXPERIMENT 7: CLUSTERING FOR ANALYSIS OF WEATHER DATA TO DECIDE TO PLAY OR NOT
STEP 1: OPEN INPUT FILE”WEATHER.ARFF” UNDER PREPROCESS TAB IN WEKA EXPLORER
STEP2: SELECT “CLUSTER” TAB IN “WEKA EXPLORER”
STEP3: CHOOSE “SIMPLE MEAN” CLUSTERER UNDER “CLUSTER” TAB.
STEP 4: CLICK “START” BUTTON UNDER “CLUSTER” TAB TO GET THE CLUSTERS AND RIGHT CLICK ON “RESULT LIST” TO VISUALIZE THE CLUSTER ASSINGMENTS.
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