Data Mining 7COM1018

School of Physics, Engineering and Computer Science
Page 1 of 7
Assignment Briefing Sheet (2020/21 Academic Year)
Section A: Assignment title, important dates and weighting

Assignment title:Flexible REF/DEFGroup or
individual:
Individual
Module title:Data MiningModule
code:
7COM1018
Module leader:Paul MoggridgeModerator’s
initials:
WJ
Submission
deadline:
18th June 2021
17:00
Target date for return of
marked assignment:
5th July 2021
You are expected to spend about40hours to complete this assignment to a
satisfactory standard.
This assignment is worth40%of the overall assessment for this module.

Section B: Student(s) to complete

Student ID numberYear Code
NOT NEEDED FOR ONLINE SUBMISSION
Notes for students
• For undergraduate modules, a score above 40% represent a pass performance at honours level.
• For postgraduate modules, a score of 50% or above represents a pass mark.
• Late submission of any item of coursework for each day or part thereof (or for hard copy submission
only, working day or part thereof) for up to five days after the published deadline, coursework relating
to modules at Levels 0, 4, 5, 6 submitted late (including deferred coursework, but with the exception of
referred coursework), will have the numeric grade reduced by 10 grade points until or unless the
numeric grade reaches or is 40. Where the numeric grade awarded for the assessment is less than
40, no lateness penalty will be applied.
• Late submission of referred coursework will automatically be awarded a grade of zero (0).
• Coursework (including deferred coursework) submitted later than five days (five working days in the
case of hard copy submission) after the published deadline will be awarded a grade of zero (0).
• Regulations governing assessment offences including Plagiarism and Collusion are available from
https://www.herts.ac.uk/about-us/governance/university-policies-and-regulations-uprs/uprs (please
refer to UPR AS14)
• Guidance on avoiding plagiarism can be found here:
https://herts.instructure.com/courses/61421/pages/referencing-avoiding
plagiarism?module_item_id=779436
• Modules may have several components of assessment and may require a pass in all elements. For
further details, please consult the relevant Module Handbook (available on Studynet/Canvas, under
Module Information) or ask the Module Leader.

School of Physics, Engineering and Computer Science
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Assignment Briefing Sheet (2020/21 Academic Year)

This Assignment assesses the following module Learning Outcomes (from Definitive Module
Document):
Successful students will typically:
2. be able to appreciate the strengths and limitations of various data mining models.
3. be able to critically evaluate, articulate and utilise a range of techniques for designing
data mining systems.
4. be able to understand and reflect on the underlying ethical and legal issues and constraints
on the holding and the use of data;
5. be able to critically evaluate different algorithms and models of data mining.
Assignment Brief:
In the workplace, you have been assigned to a new project, “recognizing supermarket purchase patterns”.
At your next meeting with management, you have been asked to explain how the FP Tree (Association
Mining) works.
Your response must include:
1. A technical explanation, articulating how the algorithm works, showing how to work out the
algorithm example by hand, using your own small example (14 marks)
2. Comments on the strength and limitations of the algorithm (8 marks)
3. Critically evaluate the algorithm for your given use case and compare with other similar
algorithms and use-cases in research, the papers should be referenced, how you do this your
choice (10 marks)
4. Describe and reflect on the ethical considerations for using this algorithm, for example could
the algorithm produce bias results; how would this happen? (8 marks)
In summary, the assignment is not to complete a data science project. Your task is to create a piece of
work explaining an algorithm (for example a video) while considering the example of using it for
recognizing supermarket purchase patterns.
The flexibility is in the type of response, (report/video), the intention to allow you to perform at your best.
In summary, your task is to explain how the FP Tree data mining algorithms works and comment on its
fitness for “recognizing supermarket purchasing patterns”.
Submission Requirements:
You may choose from the below on how you respond to this assignment,
• Video featuring a whiteboard / drawing app / pen and paper / PowerPoint (max. 16 minutes)
• Voiced over PowerPoint (max. 16 minutes)
• Large Poster with an Audio Recording (max. 16 minutes)
• Technical Document (max. 1700 words)
All length limits are flexible (+/- 10% and do not include figures, captions, and references). There are no
marks for production quality although we kindly ask that make sure the video and audio quality is fit for
purpose, (standard built in webcam and microphones should be suitable). For advise please speak to the
module leader. The videos or documents are intended for a professional environment. Accepted formats
for videos: mp4, webm, flv, mkv, avi, mov and wmv. Accepted formats for voiced over PowerPoints: pptx.

School of Physics, Engineering and Computer Science
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Accepted formats voice over if separate to PowerPoint: mp3, wav, ogg, aac, wma and m4a. Accepted
formats for posters and documents: pdf, docx, odt, png and svg. Referencing format is flexible, when using
a video, references can appear on screen or be spoken either will be accepted (please identify the title,
author, and the year).
Marks awarded for:
This assignment is worth 40% of the overall assessment for this module.
Marks will be awarded out of 40 in the proportion:
See marking scheme below.
A reminder that all work should be your own.
Videos/reports exceeding the maximum length may not be marked beyond length limit.
Type of Feedback to be given for this assignment:
Along with the marks, each student will receive individual written feedback on the online platform.

School of Physics, Engineering and Computer Science
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Mark Scheme:
1.1 Explanation Quality / Algorithm Understanding

Assessment element01-34-67-1011-14
A technical explanation,
articulating how the algorithm
works, showing how to work out
different parts of the algorithm
example by hand (14 marks)
No discernable attempt at this
element.
Little/some understanding
shown of the chosen
algorithm.
Good high-level understanding
shown of the chosen algorithm.
Very good understanding
shown of the chosen algorithm.
Excellent understanding shown
of the chosen algorithm.
Some steps of the algorithm are
explained. With some
calculations shown.
All steps of the algorithm are
explained. Most calculations
shown.
All steps are fully explained,
demonstrating all calculations
that need to occur at each step.
Limited use of visual aids (plots,
tables, graphics) for
explanation.
Appropriate visual aids (plots,
tables, graphics) have been
used thought the explanation.
Creative visual aids have been
used to articulate concisely how
each step works. This can be
hand drawn or digital.
The original source of the
algorithm has been referenced.
The original source of the
algorithm has been referenced
and recent research using the
algorithm has been cited.
Broad knowledge is
demonstrated for example
explaining how a step is like
steps taken in other algorithms.
Edge cases and/or challenging
input shown. Demonstrating
where the algorithm would fail
or be less accurate.

School of Physics, Engineering and Computer Science
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1.2 Knowledge of Strength and Limitations

Assessment element01-23-45-8
Comments on the strength and
limitations of the algorithm (6
marks)
No discernable attempt at this
element
The one or two commonly known
strength and limitations of the chosen
algorithm have been identified.
Three strength and limitations of the
chosen algorithm have been
described.
Four strengths and limitations of the
chosen algorithm have been
analyzed.
Time and space requirements of the
algorithm are briefly mentioned.
Time and space requirements of the
algorithm are analyzed. Big O
notation is mentioned.
Artificial illustrative dataset is used to
highlight strengths and limitations.
Artificial illustrative dataset and is
used to highlight strengths and
limitations. Real world datasets are
referenced regarding strengths and
limitations too.
Updates and modifications to
algorithms are discussed and recent
research papers are cited.

School of Physics, Engineering and Computer Science
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1.3 Evaluation / Comparing performance of algorithms / datasets

Assessment element01-56-10
Critically evaluate the algorithm for your use
case and compare with other similar algorithms
(5 marks)
No discernable attempt at this elementOne or two similar algorithms have been
identified.
Three similar algorithms have been identified.
The strengths and limitations of the similar
algorithms has been identified in comparison to
the chosen algorithm.
The strengths and limitations of the similar
algorithms has been identified in comparison to
the chosen algorithm and compared in relation
to the challenges in the proposed project.
Academic sources (journal and conference
papers) have been referenced to critically
evaluate the suitability of the algorithms for the
proposed project. I.e. a paper using the
same/similar algorithm on a similar use case.

School of Physics, Engineering and Computer Science
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1.4 Describing ethical Issues

Assessment element01-34-8
Describe and reflect the ethical considerations
for using this algorithm, could the algorithm
produce bias results, how would this happen? (5
marks)
No discernable attempt at this elementAn ethical issue is raised.More than one ethical issue is provided.
The ethical issues could apply to the algorithm
selected and how the algorithm would behave
different has be briefly reflected on.
How the issue would manifest itself into the
model produced by the algorithm is explained,
technical terminology is used.
Methods (likely preprocessing methods) to avoid
the ethical issue are identified.
Reference no: EM132069492

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