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COMP8410 Data Mining

Creation date：2024-05-06 17:06:41

Belonging category：计算机computer science

Tag COMP8410

Data Mining

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COMP8410 Data Mining

Assignment 2
Maximum marks 100
Weight 25% of the total marks for the course
Length
Maximum of 10 pages excluding cover sheet, bibliography and
appendices.
Layout
A4 margin, at least 11 point type size, use of typeface, margins
and headings consistent with a professional style.
Submission deadline 9:00am, Monday, 9 May
Submission mode Electronic, via Wattle
Estimated time 15 hours
Penalty for lateness 100% after the deadline has passed
Questions to: Wattle Discussion Forum

This assignment specification may be updated to reflect clarifications and modifications after it is
first issued. It is strongly suggested that you start working on the assignment right away. You can
submit as many times as you like. Only the most recent submission at the due date will be assessed.
In this assignment, you are required to submit a single report in the form of a PDF file. You may also
attach supporting information (appendices) as one or more identified sections at the end of the
same PDF file. Appendices will not be marked but may be treated as supporting information to your
report. Please use a cover sheet at the front that identifies you as author of the work using your u-
number and name and identifies this as your submission for COMP8410 Assignment 2. The cover
sheet and appendices do not contribute to the page limit or word count.
You are expected to have both an introduction and a conclusion in your report.
No particular layout is specified, but you should follow use no smaller than 11 point typeface and
stay within the maximum specified page count. Page margins, heading sizes, paragraph breaks and
so forth are not specified but a professional style must be maintained. Text beyond the page limit
will be treated as non-existent.
This is a single-person assignment and should be completed on your own. Make certain you
carefully reference all the material that you use, although the nature of this assignment suggests few
references will be needed. It is unacceptable to cut and paste another author's work and pass it off
2

as your own. Anyone found doing this, from whatever source, will get a mark of zero for the
assignment and, in addition, CECS procedures for plagiarism will apply.
No particular referencing style is required. However, you are expected to reference conventionally,
conveniently, and consistently. References are not included in the page limit. Due to the context in
which this assignment is placed, you may refer to the course notes or course software where
appropriate (e.g. “For this experiment Rattle was used”), without formal reference to original
sources, unless you copy text which always requires a formal reference to the source.
An assessment rubric is provided. The rubric will be used to mark your assignment. You are advised
to use it to supplement your understanding of what is expected for the assignment and to direct
your effort towards the most rewarding parts of the work.
Your assignment submission will be treated confidentially. It will be available to ANU staff involved
in the course for the purposes of marking. It may be shared, de-identified, as an exemplar for other
students.

Task
You are to study the supplied data set and to apply data mining processes and techniques to
discover interesting things about the data. You are to write a short report that justifies and explains
your methods in detail, presents your results, and evaluates and interprets the results you find. In
the following, the task is described in terms of what your report should contain, not in terms of the
steps you should take to carry out the assignment. In your report, similarly, you should describe the
methods used in terms of the language of data mining, not in the terms of commands you typed or
buttons you selected.

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MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 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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 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LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 FINC5090 MATH1052 SciComp Elec4622 FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 MAT 237 BISM2201 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum

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