讲解data编程设计、讲解Python，Java，C++程序、algorithm编程辅导 调试Matlab程序|辅导R语言程序

日期：2021-05-16 08:59

Assignment 3: Frequent Itemsets, Clustering,
Advertising
Formative, Weight (15%), Learning objectives (1, 2, 3),
Abstraction (4), Design (4), Communication (4), Data (5), Programming (5)
Due date: 11 : 59 pm, 28 May, 2021
1 Overview
Read the following carefully as it differs from the last assignment.
For students who are enrolled in the course COMP SCI 3306 (i.e. undergraduate
students), the assignment must be done in groups consisting of TWO
students. Please use A3-groups on MyUni to organise yourselves into groups.
If you have problems/questions regarding grouping or require assistance.
For other students who are enrolled in the course COMP SCI 7306 (i.e.
postgraduate students), this assignment must be done individually. Do not
join a group in this case.
References to sections, examples, etc. refer to the book of “Leskovec, Rajaraman
and Ullman: Mining Massive Datasets (Second Edition)”.
2 Assignment
Exercise 1 Frequent Itemsets (15+15+10+10 points)
For this exercise, you have to read Section 6.4 up to 6.4.3.
1. Implement the simple, randomized algorithm given in 6.4.1
2. Implement the algorithm of Savasere, Omiecinski, and Navathe (SON algorithm)
in 6.4.3
3. Compare the two algorithms on the datasets T10I4D100K, T40I10D100K,
chess, connect, mushroom, pumsb, pumsb star provided at
http://fimi.ua.ac.be/data/
and report the outcomes.
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COMP SCI 3306, COMP SCI 7306 Mining Big Data Semester 1, 2021
4. Experiment with different sample sizes in the simple randomized algorithm
such as 1, 2, 5, 10% and compare your results (including the result
produced by the SON algorithm).
Your approach should be as efficient as possible in terms of runtime and
memory requirements.
Report on challenges that you might have observed in the implementation
and by running experiments.
Exercise 2 Clustering (10+20 points)
1. Perform a hierarchical clustering on the one-dimensional set of points
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)
2. 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 scikitlearn
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/stable/auto_examples/datasets/plot_iris_
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.
Exercise 3 Advertising (Exercise 8.4.1) (10+10 points)
Consider Example 8.7. Suppose that there are three advertisers A, B, and
C. There are three queries x, y, and z. Each advertiser has a budget of 2.
Advertiser A only bids on x, B bids on x and y, and C bids on x, y, and z. Note
that on the query sequence xxyyzz, the optimal offine algorithm would yield a
revenue of 6, since all queries can be assigned.
1. Show that the greedy algorithm will assign at least 4 of the 6 queries
xxyyzz.
2. Find another sequence of queries such that the greedy algorithm can assign
as few as half the queries that the optimal offline algorithm would assign
to that sequence.
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COMP SCI 3306, COMP SCI 7306 Mining Big Data Semester 1, 2021
3 Procedure for handing in the assignment
Work must be handed in using Canvas (MyUni). The submission should include:
? a PDF file of your solutions for theoretical assignments. The solutions
should contain a detailed description of how to obtain the result.
For Exercise 2.2, you should properly provide comments in your code to
show your understanding.
? all source files, all the project files.
? a README.txt file containing instructions to run the code, the names,
student numbers, and email addresses of the group members (or individuals
in the case of postgraduates).
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