SIT720: Machine Learning - IT Computer Science Assignment Help

Assignment Task

Learning Outcomes

This assessment assesses the following Unit Learning Outcomes (ULO) and related Graduate Learning Outcomes (GLO):

Purpose

This assessment task is for student to apply skills for data clustering and dimensionality reduction. Students will be required to demonstrate ability in data representation, and competency in applying suitable clustering/dimensionality reduction techniques in a real-world scenario.

Assessment Task 2: Problem solving task.

Questions

1. In this dataset (tripadvisor_review.csv), we have traveller’s average feedback/rating information on 10 different categories of attraction. We are interested in finding optimal number of traveller groups based on their attraction ratings.

a. What method shall we use for solving this problem and why?

b. Does this data suffer from curse of dimensionality? Explain.

c. Find out optimal number of traveller groups, report the outcome and justify your findings.

2. Implement two alternative solutions of Q1 (c). Compare and report the findings.

3. Evaluate quality of the groupings that you have reported as a solution of Q1 (c) and Q2. Based on the evaluation outcomes, report the best solution and explain the results.

4. Quantify and print the relationship among independent variables of this dataset (tripadvisor_review.csv). Calculate two collective variables that represent the same dataset. Create a two-dimensional plot to display the relationship between these new variables and explain the plot.

5. Is there any loss of information due to the transformation performed in Q4? Explain your answer with evidence.

6. Principal component analysis applied on a given dataset, and the percentage of variance for the first N components is X%. How is this percentage of variance computed?

7. Apply component factor- and projection-based dimensionality reduction approaches on the given dataset (tripadvisor_review.csv) for creating three collective variables. Does this new feature space improve the grouping of travellers compared to original dataset? Present your results with appropriate evidences.

8. Let’s consider the data shown in the Figure 1.

a) Is it possible to obtain the cluster shown in the figure by k-means clustering (k = 6)? Provide evidence including code and explanation to justify your findings.

b) Explore state-of-the-art clustering methods (explore recent research articles) that can produce better results than k-means for this problem? Describe the selected approach, evaluate performance and report your findings.

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