Assessment item 3 – Practical Analysis and Report Writing

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Value: 15%

Due Date: 05-Oct-2020

Return Date: 26-Oct-2020

Submission method options: Alternative submission method

TASK

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Assessment Description

This assessment task, is related to Topic 1, and Topics 5-9.

Task 1 : Practical Analysis [50 marks]

There are two steps to complete in this task:

Step 1: You are required to perform a data mining task to evaluate different classification algorithms. Load the soybean.arff data set into Weka and compare the performance on this data set for the following classification algorithms:

• Naive Bayes
• HoeffdingTree
• SVM ( or SMO)
• J48

Step 2: From step 1 outputs, write a report that shows the performance of the different algorithms and comment on their accuracy using the confusion matrix and other performance metrics used in Weka. In your report consider:

• Is there a difference in performance between the algorithms?
• Which algorithm performs best?

Your report should include the necessary screenshots, tables, graphs, etc. to make your report understandable to the reader.

Task 2: Data Mining Report [50 marks]

Topic: Security, Privacy and Ethics in Data Mining.

In this task, you are required to read the journal articles provided below and write a short discussion paper based on the topic of security, privacy and ethics in data mining. You must:

• identify the major security, privacy and ethical implications in data mining;
• evaluate how significant these implications are for the business sector; and
• support your response with appropriate examples and references (at least 2 additional references should be sought in addition to the ones below).

The recommended word length for this task is 700 to 1000 words.

Journal articles:

Ryoo, J. ‘Big data security problems threaten consumers’ privacy’ (March 23, 2016) theconversation.comhttp://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798

Tasioulas, J. ‘Big Data, Human Rights and the Ethics of Scientific Research’ (December 1, 2016) abc.net.auhttp://www.abc.net.au/religion/articles/2016/11/30/4584324.htm

RATIONALE

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This assessment task will assess the following learning outcome/s:

• be able to identify and analyse business requirements for the identification of patterns and trends in data sets.
• be able to appraise the different approaches and categories of data mining problems.
• be able to compare and evaluate output patterns.
• be able to explore and critically analyse data sets and evaluate their data quality, integrity and security requirements.
• be able to compare and evaluate appropriate techniques for detecting and evaluating patterns in a given data set.

MARKING CRITERIA AND STANDARDS

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The grade you receive for this assessment as a whole is determined by the cumulative marks gained for each question. The tasks in this assessment involve a sequence of several steps and therefore you will be marked on the correctness of your answer as well as clear and neat presentation of your diagrams, where required.

Practical Analysis and Report Writing

Criteria	HD	DI	CR	PS	FL
Task 1 : Practical Analysis [50 marks]	The student has thoroughly understood the classification methods, providing a detailed description of the methods and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates thorough understanding of the classification methods as applied to the given data set	The student has understood the classification methods, providing a detailed description of the methods and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates good understanding of the classification methods as applied to the given data set.	The student has understood the classification methods, providing a description of the methods and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates understanding of the classification methods as applied to the given data set.	The student has understood the classification methods, providing a description of the methods and its output on the given data set. The discussion involving the validation and accuracy of the model shows basic understanding of the classification methods as applied to the given data set.	The student has not fully understood the classification methods, providing a description of the methods and its output on the given data set. The discussion is not fully involving the validation and accuracy of the model shows basic understanding of the classification methods as applied to the given data set.
Task 2 : Report Writing [50 Marks]	Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions that are logically supported by examples and best practice. Answers succinctly integrate and link information into cohesive and coherent piece of analysis and consistently use correct data mining terminologies and sophisticated language.	Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions that are logically supported by examples and best practice. The answers are logically structured to create cohesive and coherent piece of analysis that consistently use correct data mining terminologies.	Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions that are generally logically supported by examples and best practice. The answers are generally logically structured to create a comprehensive, mainly descriptive piece of analysis. Some use of correct data mining terminologies.	Demonstrate an ability to analyse, reason and discuss most concepts to draw justified conclusions that are generally logically supported by examples and best practice. The answers are partially structured into loosely-linked rudimentary sentences to create a comprehensive, descriptive piece of analysis. Some use of correct data mining terminologies.	Demonstrates incomplete/insufficient research in security, privacy and ethics in data mining with incomplete responses supported by no or irrelevant examples, incorrect terminologies and poor/inadequate references.

PRESENTATION

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You are recommended to write the answers in a word document and submit it via Turnitin. You can also submit your document in pdf format as well.

Your answers to the questions should be precise but complete and informative.

Task 1

It should also include a background that describes the dataset eg the number of attributes, number of instances, the distribution of the class attribute and so on. It should also describe how the experiment was set up, whether or not default parameters for each of the algorithms where used or not.

A brief summary of each algorithm should be provided with screenshots of the results.

An analysis that compares the results should be provided.

Marks distributed as follows: 10 marks for background, 7.5 marks each for an algorithm (for a total of 20 marks), 10 marks for the analysis that compares the results.

Task 2

Your report should have an introduction, a main section covering each of the three requirements and a conclusion.

Your report should also include at least 2 additional relevant references.

Marks are distributed as follows: 7.5 marks for introduction, 10 marks each for the 3 requirements, 7.5 marks for the conclusion, 5 marks for logical structure

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