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.