SCC403 – Data Mining
Coursework Assignment
1 Introduction
The objective of the assignment is to conduct data analysis on a real life data set, which concerns
climate. This includes selection and justification of the specific methods for data pre-processing,
clustering and classification, their implementation and analysis of the results and well annotated
code. It is expected that your analysis should include:
• data pre-processing (to achieve top marks a well justified variety of specific pre-processing
techniques is expected)
• clustering (to achieve top marks a well justified variety of specific clustering techniques is
expected)
• data classification (to achieve top marks a well justified variety of specific classification techniques
is expected)
All choices must be justified through analysis and comparison. Analysis and understanding of
the methods, algorithms and the overall process are the most important elements in addition to the
implementation skills (code, presentation) and the results. You are expected to critically analyse
the results of applying these techniques, and demonstrate a clear understanding of the purpose
and processes of data analysis.
In addition to your report, please submit your source code, including comments, plots, and an
analysis of the results. You are free to use libraries, but students that implement methods from
scratch will be more likely to receive higher grades.
We recommend using Python, since it is the language that we are using in the labs, but you
are free to use a different languages if you prefer. Note, however, that we may need to contact you
for clarification, if we do not understand your code, or if we believe that your code is not running
correctly.
2 Data-Set
You are expected to use the set of climate data provided in the file 0SCC403CW ClimateData.txt0
.
This data is a subset of publicly available (from https://www.meteoblue.com/) data about climate
in Basel, Switzerland which contains 1763 records (18 features, a 18 dimensional vector) of data
from the summer and the winter seasons of the period from 2010 to 2019 period. The meaning of
each column of data is listed below:
• Temperature (Min) oC.
• Temperature (Max) oC.
• Temperature (Mean) oC.
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• Relative Humidity (Min) %.
• Relative Humidity (Max) %.
• Relative Humidity (Mean) %.
• Sea Level Pressure (Min) hP a.
• Sea Level Pressure (Max) hP a.
• Sea Level Pressure (Mean) hP a.
• Precipitation Total mm.
• Snowfall Amount cm.
• Sunshine Duration min.
• Wind Gust (Min) Km/h.
• Wind Gust (Max) Km/h.
• Wind Gust (Mean) Km/h.
• Wind Speed (Min) Km/h.
• Wind Speed (Max) Km/h.
• Wind Speed (Mean) Km/h
The lectures and tutorials will provide you with the necessary tools to conduct your analysis.
You may also include additional analysis methods that you have researched separately,that may
help derive your conclusion (this is not compulsory).
It is expected that your analysis will include;
1. pre-processing,
2. clustering, and
3. classification of the data.
For the classification task you will need to provide suitable and logical labels to different classes
of data, for example, “cold, dry, windy”, “wet and windy”, “dry, warm with no wind”, etc. You
are expected to critically analyse the results of applying these techniques, and demonstrate a clear
understanding of the purpose and processes of data analysis.
The deadline for submission is: 4pm, 11 December 2020, Friday. The cut-off deadline
is 4pm, 14 December 2020, Monday (with late submission penalty incurred which is 1 letter grade or
10%). Submissions after this deadline cannot be accepted according to the University regulations.
In case your code is unclear to us you may be contacted for interview. If you fail to reply or
attend the interview your code could be marked as “not working”. Since the deadline is in the end
of the Michaelmas term, it is acceptable to attend the interview by teleconference in case you are
not in Lancaster.
代写SCC403程序
3 Marking Scheme
For the overall report marks are allocated as follows:
• Structure and presentation (5%)
• Language and style (6%)
• Level of understanding (12%)
• Depth of analysis (12%)
• Use of literature and references (5%)
Each of the three parts (pre-processing, clustering, and classification) will form 20% of the total
mark split as follows:
• Working, well annotated code and results (8%)
• Justification of selected methods (6%)
• Independent research and use of methods not given in the lectures (6%)
At the end of this document there is an Appendix, which explains what a mark means in
Lancaster University and includes suggestions for a well-written report.
The length of the report should not exceed 6 pages. You can use double column format, e.g.
the so-called IEEE style as described in the Appendix. You may include an Appendix (2 pages
maximum) after the main report (8 pages in a total).
4 Tasks description
4.1 Pre-processing
Pre-processing includes data standardisation and/or normalisation, detecting and removing anomalies,
missing values (if any), feature selection and/or extraction (the latter are optional). Preprocessing
provides an insight into the data correlations and patterns.
If you choose to use the Principle Component Analysis (PCA) method, you can extract new,
orthogonal (independent) features, which are a linear combination of the original ones (which carry
a clear physical meaning, such as temperature or pressure). If you choose to use PCA, please,
comment on the amount of variance, interpretability and the link with the original features. You
should also plot the results using, for example, the one or two of the principle components which
contain most of the variance.
4.2 Clustering
Choose at least two clustering algorithms. To achieve top marks one of the methods should be
from independent research.
Develop the programme and explain the functionality of the algorithms in as much detail as
you can. Compare the results and limitations of each of the algorithms that you have used.
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4.3 Classification
Climate data set does not have specific class labels - you may label the clusters you obtained in the
previous Task with some meaningful names, e.g. ”Cold windy days”, ”Hot dry days”. Alternatively,
you may choose to use numbers 1,2,... or letters.
The task is to train at least two classifiers of your choice on a part of the data (again, you may
choose how to split the data), perform cross-validation, evaluate the performance of the classifiers
and report this.
When analysing the performance of the classifiers you should use precision/recall, F1 score
and classification accuracy as well as computational time for training the classifier as a measure of
complexity.
5 Additional Comments
You must report in an “acknowledgements” section the use of any libraries, readily available online
code, and code from online tutorials. Additionally, you are free to discuss your work with colleagues,
but you must also report in the “acknowledgments” section if anyone has helped you significantly.
Remember that using others’ work without giving the due credit is an act of plagiarism, and it is
not a good academic practice.
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Presenting someone else’s work as your own in an
assignment without proper citation of the source is an act of
plagiarism. More information about Lancaster University
Plagiarism Framework can be found at
https://www.lancaster.ac.uk/academic-standards-andquality/information-and-resources/policies-andguidelines/plagiarism-framework/
APPENDIX
Example of the style of the report
Title of the Report
Subtitle as needed
Author’s names, Student number
line 1: dept. name of organization
line 2-name of the programme and module
Abstract— Briefly describe the outline of your report.
I. Introduction
Here you have to provide the background review. of the
existing approaches stressing the ones that have been actually
used. Critically analyse and compare alternative techniques and
methods. Try to go beyond what was given in the lectures
using external sources and references.
II. Pre-processing
Here you have to provide a description and description and the
results of pre-processing techniques that are relevant and
stress those that you actually used in your work. Provide the
software code that you used to obtain the results in an
Appendix. Do not forget to justify your choice.
III. Clustering
Here you have to provide a description and the results of
clustering techniques that are relevant and stress those that you
actually used in your work. Provide the software code that you
used to obtain the results in an Appendix. A very important
part of your report is the critical analysis of the results. Why
have you used the stated methods? What are the advantages
and limitations of the algorithms that you have used?
IV. Classification
Here you have to provide a description and the results of
classification techniques that are relevant and stress those that
you actually used in your work. Provide the software code that
you used to obtain the results in an Appendix. A very
important part of your report is the critical analysis of the
results. Why have you used the stated methods? What are the
advantages and limitations of the algorithms that you have
used?
V. Conclusion
Describe briefly what has been done, with a summary of the
main results. Discuss here possible future developments (what
you would have done more). What is distinctive about the
results you have obtained?
VI. References
The template will number citations consecutively within
brackets [1]. The sentence punctuation follows the bracket [2].
Refer simply to the reference number, as in [3]—do not use
“Ref. [3]” or “reference [3]” except at the beginning of a
sentence: “Reference [3] was the first ...”
Number footnotes separately in superscripts. Place the
actual footnote at the bottom of the column in which it was
cited. Do not put footnotes in the reference list. Use letters for
table footnotes.
[1] J. Han, M. Kamber, Data Mining: Concepts and Techniques,
Morgan Kaufmann, 2001
[2] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Heidelberg,
Germany: Springer Verlag, 2001
[3] Angelov, P.: Autonomous Learning Systems: From Data Streams to
Knowledge in Real Time. John Wiley and Sons (2012).
[4] Angelov, P.: Outside The Box:An Alternative Data Analytics FrameWork.
Journal of Automation, Mobile Robotics & Intelligent Systems.
Vol. 8, 29–35.
Appendix
Please, provide here a well annotated working code.
Please include here additional experimental results
or additional details.
5
What a Mark Means in Lancaster University
70 + (Distinction)
Critical Understanding of Topic
Excellent understanding and exposition of relevant
issues; insightful and well informed, clear evidence of
independent thought; good awareness of nuances and
complexities; appropriate use of theory.
Structure of Research
Substantial evidence of well implemented independent
research and / or Substantial evidence of well selected
evidence to support argument.
Use of Literature
Excellent use of literature to support argument /points.
Conclusion
Excellent; clear implications for theory and/or practice.
Language
Excellent; a delight to read.
Structure and Presentation
Arguments clearly structured and logically developed;
sensible weighting of parts; meaningful diagrams;
properly formatted references.
65 – 69% (Very Good Pass)
Critical Understanding of Topic
Clear awareness and exposition of relevant issues; some
awareness of nuances and complexities but tendency to
simplify matters; based on appropriate choice and use of
theory.
Structure of Research
Some evidence of independent research reasonably well
implemented and / or some evidence of identification of
suitable evidence to support argument.
Use of Literature
Good use of literature to support arguments.
Conclusion
Very good; draws together main points; some
implications for theory and/or practice
Language
Carefully written; negligible errors.
Structure and Presentation
Arguments clearly structured and logically developed;
good weighting of parts; meaningful diagrams; properly
formatted references.
60 – 65% (Good Pass)
Critical Understanding of Topic
Shows awareness of issues and theories; attempts at
analysis but tendency to lapse into description
Structure of Research
Some evidence of independent research reasonably well
implemented and / or some evidence of identification of
suitable evidence to support argument.
Use of Literature
Use of standard literature to support arguments.
Conclusion
Reasonable conclusion that summarises essay; a few
implications for theory and/or practice.
Language
A few errors; generally satisfactory.
Structure and Presentation
Arguments reasonably clear but undeveloped; some
meaningless diagrams or poor structure.
50 – 59% (Pass)
Critical Understanding of Topic
Work shows understanding of topic but at superficial
level; no more than expected from attendance at lectures;
some irrelevant material; too descriptive.
Structure of Research
Insufficient evidence of independent research and / or
very limited evidence used to support argument.
Use of Literature
Use of secondary literature to support arguments.
Conclusion
Conclusion does not do justice to body of essay; too
short; no implications.
Language
Some errors; grammar and syntax need attention.
Structure and Presentation
Arguments not very clear; poor organisation of material;
poor use of diagrams; poor referencing.
45 – 49% (Marginal Fail)
Critical Understanding of Topic
Establishes a few relevant points but superficial and
confused; much irrelevant material; very little or no
understanding of the issues raised by the topic or topic
misunderstood; content largely irrelevant; no choice or
use of theory; essay almost wholly descriptive; no grasp
of analysis with many errors and/or omissions.
Structure of Research
No evidence of independent research and / or No attempt
to identify suitable evidence to support argument.
Use of Literature
Relies on a superficial repeat of class notes.
Conclusion
No recognisable conclusion.
Language
Frequent errors; needs urgent attention.
Structure and Presentation
Arguments often confused and undeveloped; no logical
structure; very poor organisation of material; many
meaningless diagrams; negligible referencing.
0 – 44% (Clear Fail)
Critical Understanding of Topic
Establishes a few relevant points but superficial and
confused; much irrelevant material; very little or no
understanding of the issues raised by the topic or topic
misunderstood; content largely irrelevant; no choice or
use of theory; essay almost wholly descriptive; no grasp
of analysis with many errors and/or omissions.
Structure of Research
No evidence of independent research and / or No attempt
to identify suitable evidence to support argument.
Use of Literature
No significant reference to literature.
Conclusion
No recognisable conclusion.
Language
Frequent errors; needs urgent attention.
Structure and Presentation
Arguments often confused and undeveloped; no logical
structure; very poor organisation of material; many
meaningless diagrams; negligible referencing.

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