ITC516 | PRACTICAL AND WRITTEN REPORT ONLY PART 2 ASSESSMENT 3 | DATA MINING AND VISUALISATION

Task

Part 1 – Online Quiz

There will be an online quiz during Week 11 (22nd Jan – 28th Jan). The online quiz must be attempted by the students individually on the Interact site for ITC516. The Quiz is worth 5 marks of the overall marks available for assessment 3.

Students need to attempt and finish the online quizzes within the specific date and time window.

You will have an hour (1 hour) to complete the quiz which consists of 30 multiple choice questions. Once you start your quiz, you must complete it in one sitting. You only have one attempt at the quiz.

The topics of the online quizzes are:

Introduction to Data Mining

Data Interpretation

Knowledge Representation

Overview of Basic Algorithms and Credibility

Decision Trees

Classification Rules

Association Rules

Linear Models

Instance-Based Learning and Clustering

                                                                                                 

Part 2 - Practical and Report

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 three classification algorithms:

Decision Tree

Naive Bayes

k-Nearest Neighbour

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.

The task is worth 15 marks of the overall marks available for assessment 3.

Rationale

These tasks aim to assess your progress towards:

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;

be able to explain the importance of current and future trends likely to affect data mining and visualisation.

Marking criteria

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.

Part 1 - Online Quiz

This part is a series of multiple choice questions. Each correct answer will score 1 mark. Marks will not be deducted for incorrect answers.

Most quizzes will involve multiple choice or true/false type questions, although quizzes may include other contents. Marks will be given based on the correctness of the answers. The Test Centre will be marking automatically and you will receive marks according to the following criteria: HD - At least 85% answers were correct

DI - At least 75% answers were correct

CR - At least 65% answers were correct

PS - At least 50% answers were correct

Part 2 - Practical and Report

Criteria	HD		DI	CR		PS		FL
	The student has		The student has
	thoroughly		understood the	The student has		The student has		The student
	understood the		classification	understood the		understood the		has not
	classification		methods,	classification		classification		fully understood
Demonstrate	methods, providing		providing a	methods,		methods,		the classification
an ability to	a detailed		detailed	providing a		providing a		methods,
	description of the		description of the	description of the		description of the		providing a
analyse,
	methods and its		methods and its	methods and its		methods and its		description of the
reason and
	output on the given		output on the	output on the		output on the		methods and its
discuss the
	data set. The		given data set. The	given data set.		given data set.		output on the
concepts
	discussion		discussion	The discussion		The discussion		given data set. The
learned in
	involving the		involving the	involving the		involving the		discussion is not
the subject
	validation and		validation and	validation and		validation and		fully involving the
and be able
	accuracy of the		accuracy of the	accuracy of the		accuracy of the		validation and
to use
	model		model	model		model shows		accuracy of the
classification
methods on	demonstrates		demonstrates	demonstrates		basic		model shows basic
	thorough		detailed	understanding of		understanding of		understanding of
datasets.
	understanding of		understanding of	the classification		the classification		the classification
	the classification		the classification	methods as		methods as		methods as applied
	methods as applied		methods as	applied to the		applied to the		to the given data
	to the given data		applied to the	given data set.		given data set.		set.
	set		given data set.

Presentation

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

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

Each question should be answered individually with the corresponding label to indicate the tasks completed e.g. Task 1 a.

Task-2 report should be precise but complete and informative and the number or words within 1500 -1800 words.