COMP SCI 7306 PageRank

Assignment 3: PageRank, Frequent Item-Sets
and Clustering
Formative, Weight (15%), Learning objectives (1, 2, 3),
Abstraction (4), Design (4), Communication (4), Data (5), Programming (5)
Due date: 11 : 59 pm, 25 March, 2022
1 Overview (Attention, Different To Previous
Assignments)
This assignment must be done individually. This means all the rules regarding
individual submission will apply and the submission must be solely your own
work. Therefore, we will not use the groups on MyUni. You will need to submit
on the assignment page as an individual.
2 Assignment
Exercise 1 Frequent Item-Sets (30 points)
Suppose there are 100 items, numbered 1 to 100, and also 100 baskets, also
numbered 1 to 100. Item i is in basket b if and only if i divides b with no
remainder. Thus, item 1 is in all the baskets, item 2 is in all fifty of the even￾numbered baskets, and so on. Basket 12 consists of items 1, 2, 3, 4, 6, 12, since
these are all the integers that divide 12. Answer the following questions:

1. If the support threshold is 5, which items are frequent?
2. what is the confidence of the following association rules?
   (a) {5, 7} → 2.
   (b) {2, 3, 4} → 5.
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   COMP SCI 7306 Mining Big Data Trimester 1, 2022
   Exercise 2 PageRank (40 points)
3. Implement the PageRank Algorithm as discussed in Section 5.1 and 5.2
   (Leskovec, Rajaraman and Ullman) in JAVA, Python or C++. Your im￾plementation should make use of the improvements regarding efficiency
   and the methods of dealing with dead-ends and spider traps. There are
   several PageRank implementations available on the web. You have to do
   your own implementation without using any code from other sources.
4. Run your algorithm on the Google Web Graph 2002 available at
   http://snap.stanford.edu/data...
   and provide a file listing the PageRank for each node. Report separately,
   the ordered list of the ten nodes having the largest PageRank
   Your approach should be efficient as possible in terms of runtime and memory
   requirements.
   Note: you are asked to implement the algorithm from scratch, without using
   third party implementations/ libraries.
   Exercise 3 Clustering (30 points)
5. Perform a hierarchical clustering on the one-dimensional set of points and
   show your results (best to use dendrograms)
   1, 4, 9, 16, 25, 36, 49, 64, 81.
   assuming the clusters are represented by their centroid (average), and at
   each step the clusters with the closest centroids are merged. (Exercise
   7.2.1)
6. Implement the K-means algorithm and carry out experiments on the Iris
   dataset (note that you are not allowed to use the libraries such as scikit￾learn to implement the algorithm itself, but you are free to compare your
   results with such). The dataset can be accessed from scikit-learn library.
   You may follow the instructions at the following link:
   https://scikit-learn.org/stab...
   dataset.html
   a) Plot the K-means clustering results by plotting the first 2 dimensions
   of the input data as well as the converged centroids.
   b) Provide some discussions about how you picked the value of K in the
   K-means algorithm.
   Note: You should only use the 4 input features in the Iris dataset to
   cluster them, and not the labels. Also, similar to previous exercise, you
   are asked to implement from scratch without using third-party implemen￾tations/ libraries.
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   COMP SCI 7306 Mining Big Data Trimester 1, 2022
7. General assignment submission guidelines
   As stated in the beginning of the assignment, work MUST be submited using
   the group’s interface on MyUni, and a single submission per group, ONLY. The
   submissions will include the following, at minimum:
   • PDF file of your solutions for theoretical assignments. The solutions
   should contain detailed description of how to obtain the result.
   • All source files, all the project files.
   • PDF or txt file with descriptions of your implementations to understand
   your code.
   • Files containing the results of your algorithms on the provided datasets.
   • PDF or txt file of your computation times of the algorithms on provided
   datasets.
   • a README.txt file containing instructions to run the code, student ID,
   and email address.
   • the submissions that do not follow the above guidlines may lose points
   accordingly.
   Please do not hesitate to reach out using the discussion forum, workshops, or
   the contact details of the teaching assistants on the home page of MyUni, should
   you have any questions or concerns.
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