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DATA MINING ITS61504
创建日期：2024-04-23 17:10:36
标签： ITS61504
DATA MINING

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 DATA MINING

ITS61504

Instruction to Candidates:
1. 1. Answer ALL questions
2. This is an open book examination, student is not allowed to transcribe directly (cut and
paste) any material from another source into their submission.
3. The Turnitin similarity for this module is 20% overall and lesser than 5% from a single
source excluding program source codes.
4. Severe disciplinary action will be taken against those caught violating assessment rules
such as colluding, plagiarizing or transcribing.
5. The final assessment answers handed in should be within 5 -12 pages in total for non-
programming modules, with a spacing of 1.5 and a font of 12pt Times New Roman.
6. Submission link is here. (Do not submit the question paper)

7. The breakdown of exam questions by Module Learning Outcome(s) and its associate
weightage is as follows:
MLO Section(s)/ Question(s) Marks
MLO1 Question 1 / 20
MLO2 Question 2 / 20
MLO3 Question 3 / 20
MLO3 Question 4 / 20
MLO4 Question 5 / 20
TOTAL / 100

8. Start each answer on a separate page.
9. Complete the front cover of the examination answer booklet and question paper. Write
the question numbers attempted on the front cover of the answer booklet.






Page 2 of 6
Data Mining
ITS61504
Lorita Angeline
202108FE


Part I: Association Rule Mining
1. Table 1 shows the record for computer purchase. Manually calculate all items sets (from
one item to the maximum number of items you can find) using Apriori method. Prune the
item sets with minimum support of 35% and minimum confidence of 75%. (20 marks)
a) Manually calculate all itemset (10 marks)
b) Prune itemset with minimum support of 35% (5 marks)
c) Prune itemset with minimum confidence of 75% (5 marks)

Table 1: Computer purchase transactional record
TID Age Income Student Credit Rating Class (buy comp)
1 lessEqual30 High No Fair No
2 lessEqual30 High No Excellent No
3 31… 40 High No Fair Yes
4 greatThan40 Medium No Fair Yes
5 greatThan40 Low Yes Fair Yes
6 greatThan40 Low Yes Excellent No
7 31… 40 Low Yes Excellent Yes
8 lessEqual30 Medium No Fair No
9 lessEqual30 Low Yes Fair Yes
10 greatThan40 Medium Yes Fair Yes
11 lessEqual30 Medium Yes Excellent Yes
12 31… 40 Medium No Excellent Yes
13 31… 40 High Yes Fair Yes
14 greatThan40 Medium No Excellent No
15 31… 40 Medium Yes Fair Yes




Page 3 of 6
Data Mining
ITS61504
Lorita Angeline
202108FE


Part II: Case Study

In metropolitan cities like Kuala Lumpur, the prospective home buyer considers several
factors such as location, size of the land, proximity to parks, schools, hospitals, power
generation facilities and most importantly the house price. House price prediction is a
significant financial decision for individuals working in the housing market as well as for
potential buyers. From investment to buying a house for residence, a person investing in the
housing market is interested in the potential gain. Table 2 shows the property listing and the
factors for house price prediction. The full dataset is available on TIMeS (data_kl.csv)
Features:
• Rooms: Number of rooms
• Price: Price in Ringgit Malaysia (MYR)
• Distance: Distance from KL downtown
• Bedroom2: Number of Bedrooms
• Bathroom: Number of Bathrooms
• Car: Number parking space
• Landsize: Land size

Table 2: Property listing and the factors for house price prediction
Rooms Price Distance Bedroom2 Bathroom Car Landsize
2 1480000.0 2.5 2.0 1.0 1.0 202.0
2 1035000.0 2.5 2.0 1.0 0.0 156.0
3 1465000.0 2.5 3.0 2.0 0.0 134.0
3 850000.0 2.5 3.0 2.0 1.0 94.0
4 1600000.0 2.5 3.0 1.0 2.0 120.0
2 941000.0 2.5 2.0 1.0 0.0 181.0
3 1876000.0 2.5 4.0 2.0 0.0 245.0
2 1636000.0 2.5 2.0 1.0 2.0 256.0
3 1000000.0 2.5

1.0 1.0 238.0
2 745000.0 2.5 2.0 1.0 1.0 113.0
1 300000.0 2.5 1.0 1.0 1.0 0.0
2 1097000.0 2.5 3.0 1.0 2.0 220.0
2 542000.0 2.5 2.0 1.0

195.0
2 760000.0 2.5 2.0

1 481000.0 2.5 1.0 1.0



Page 4 of 6
Data Mining
ITS61504
Lorita Angeline
202108FE



(Question 2 – 5 are based on the case study, to predict house pricing)

2. Clean the dataset data_kl.csv using pre-processing techniques in R. Describe each
detected noises and anomalies that are existed in the full dataset. (20 marks)

3. Write a piece of program in R to develop a prediction model that predicts house pricing
for a new property listing. You are allowed to amend the dataset (justify your amendments).
Apply your model on our new house on the listing, newList: (20 marks)
newList <- data.frame(Room = 2,
Distance = 2.5,
Bedroom2 = 2,
Bathroom = 1,
Car = 0,
Landsize = 181)

4. Based on the original dataset (or your updated dataset), can we apply any regression
modelling? Which type(s) of regression modelling do you suggest? Justify your opinion with
a clear description. Use and modify a piece of program in R to conduct a regression
modelling. Apply your model on our new house, newList. (20 marks)

5. Which model is preferable for house price prediction? Evaluate and describe the
performance of both models (developed in question 3 and 4) using performance metric.
(20 marks)

- END OF QUESTION PAPER -



Page 5 of 6
Data Mining
ITS61504
Lorita Angeline
202108FE


Submission Requirements
1. Font type : Times New Roman
2. Font size : 12
3. Line spacing : 1.5
4. Alignment : Justify Text
5. Document type : .pdf, .R
6. Number of pages : 5 – 12 pages
7. A report of your answer should consist of the following (in order):
a) Cover page (Name, ID, Date, Signature, Score)
b) Report of your answer script
c) Appendixes (line spacing = 1.0)
• R programming
• List of references (APA format)
• Report of similarity score (percentage of similarity score from each source needs
to be shown)
8. Start each question on a separate page.
9. All figures and tables are labelled properly.
10. File naming conventions: StudentName_FinalAssessment

Notes:
• Include in-text citation to support your answers and add the list of references at the end of your
report (APA format). The list of references is to be alphabetized by the first author's last name, or
(if no author is listed) the organization or title.
• You are required to add screenshots of the code and results for each question.
• The program code must be appended to the main report (put in Appendix).
• The original program files (*.R) are required to be attached to the report upon submission.


No Student Name Student ID Date Signature Score
1



Page 6 of 6
Data Mining
ITS61504
Lorita Angeline
202108FE


ITS61504 Data Mining
Final Exam - Alternative Assessment
Marking Rubric (August 2021)
Criteria Excellent Good Average Poor
(90 – 100) (75 – 89) (40 – 74) (0 – 39)
Q 1: Describe
rule
association
mining

(MLO 1)
All itemset is
calculated, Apriori
method is applied
correctly and the
solution is clearly
elaborated in a step-by-
step manner. The
similarity is less than
2%.
All itemset is
calculated, Apriori
method is applied
correctly and the
solution is NOT clearly
elaborated in a step-by-
step manner. The
similarity is less than
2%.
Two itemset is
calculated, Apriori
method is NOT applied
correctly and the
solution is NOT clearly
elaborated in a step-by-
step manner. The
similarity is between
2% to 4%.
One or no itemset is
calculated, Apriori
method is NOT applied
correctly and the
solution is NOT clearly
elaborated in a step-by-
step manner. The
similarity is greater than
or equal to 5%.
Q2: Pre-
processing
techniques

(MLO 2)
All types of noise and
anomalies are detected
with high degree of
accuracy. The code is
applied correctly and the
solution is clearly
elaborated in a step-by-
step manner. The
similarity is less than
2%.
All types of noise and
anomalies are detected
with moderate degree of
accuracy. The code is
applied correctly and the
solution is NOT clearly
elaborated in a step-by-
step manner. The
similarity is less than
2%.
Some noise and
anomalies are detected
with moderate degree of
accuracy. The code is
applied correctly and the
solution is NOT
elaborated in a step-by-
step manner. The
similarity is between
2% to 4%.
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MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 BCPM0008 ECN 620 QBUS6830 ECSE-415 ECON5102 MGMT20005 MAT 653 ECO 480 COMP 5/600 CSSE 5600 ME 571 7CCMMS61 CS 550 COMP2865 MATH3075 STATS 506 QTM 220 ISYS2120 FIT5106 STAT 153 MKTG3112 ECON2102 ELE2018 COMP3031 MSIN0010 SCC461 MATH237 CS246 CSC3065 CSE 101 ICTWEB411 COMP9313 ECOS 2025 FN620 MATH11111 MATH 3018 KE1068 FINA 6112 C++ MISM 6202 MSIN0105 CSC8631 MECO6935 COMP4121 ACCT2011 DPBS1150 EEL 3701C F19PL OPM 661 CSE 310 CS918 BIOL30001 ACCT3563 BANK3600 MET CS622 MATH367 ACCT5907 Continuing COMP5048 COMP61021 CS3334 GSOE9210 CSCI-570 COMP 5322 HW 2 ALY-6020 IEOR 4706 DATA 200 EBUS504 CGE12412 COMP4108 7SSMM800 ECMM171P MATE50002 CS225 RS 6003 BH2274 ELEC3111 MAT6669 ECON-UA 227 PHIL 1012 7SSMM603 TB028A P6 ECS708 CSC 427 CS6476 CSE 250A A1A2 ITS61504 EECS 376 model HW6 UV0010 ECON 6074 MG4F7 CMP-7030Y INFO3008 BFIP013 ECON 5068 MATH6002 CS3483 PSTAT 131 C31FM CSOR W4246 AAEC 5307 DATA ELEC362 COMSM0047 ECN6006 MA20224 BST351 MATH 323 MATH323 AA2021 COMP3811 COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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