Description
General Introduction:
This project contains 2 parts. In the first part, you are asked to implement PCA (Principle
Components Analysis) algorithm, project the high-dimensional data to 2 dimensions, and
plot the 2-dimensional data points. In the second part, you are asked to implement
Apriori and association rule generation algorithms.
Each team should submit codes and a hard copy report, and give a demo. Demo details
will be released two days before the demo date on Piazza. You need to submit the hard
copy report during demo, and submit codes to departmental server before 1pm September
30.
Part 1: Dimensionality Reduction
Dataset Description:
In this part, you are expected to conduct dimensionality reduction on three biomedical
data files (pca_a.txt, pca_b.txt, pca_c.txt), which can be found on Piazza.
In each file, each row represents the record of a patient/sample; the last column is the
disease name, and the rest columns are features. Note that your code should be able to
handle the data with different numbers of rows/columns.
Required Tasks:
Please take the following steps:
1. You are free to use your preferred programming language(s). You need to
implement the PCA algorithm by yourself. Applying existing package(s) to
conduct PCA directly will not receive any credit. If you are not sure about
whether it is OK to use a certain function, please post your question on Piazza.
2. Implement PCA and then run it on three data files (pca_a.txt, pca_b.txt, pca_c.txt)
to get the two-dimensional data points. For each dataset, draw the data points with
a scatter plot, and color them according to their disease names.
3. Apply existing packages to run SVD and t-SNE algorithms (Do not need to
implement them by yourself) and get the two-dimensional data points. Visualize
the data points of the two algorithms on the three datasets in the same way as the
visualization of PCA results in step 2.
4. Prepare your submission. Create a folder named PCA, in the folder you should
include:
a. Report: A pdf file named as PCA_report.pdf. The report should contain:
i. Nine scatter plots from three datasets and three algorithms. Label
them properly by the dataset name and algorithm name in each plot.
ii. Describe the flow of your PCA implementation briefly, and discuss
the results obtained by different algorithms.
b. A folder named Code, which contains all codes used in this part. Inside the
folder, please have a file README to describe how to run your code.
Part 2: Association Analysis
Dataset Description:
The dataset is about gene expressions (association-rule-test-data.txt) and can be found on
Piazza. Each row stands for a patient/sample. The last column is the disease name. For
the rest columns, they are gene expressions with values Up or Down (Binary Value). For
example, the row “Down Down Down Up … AML” can be interpreted as “G1_ Down
G2_ Down G3_ Down G4_Up … AML”, and AML is a disease name.
Required Tasks:
1. Implement the Apriori algorithm to find all frequent itemsets. Report the number
of frequent itemsets for support of 30%, 40%, 50%, 60%, and 70%, respectively.
Please see Template.pdf for details.
You should not directly call any existing function or package that implements
Apriori. Apriori algorithm should be implemented by yourself. If you are not
sure about whether it is OK to use a certain function, please post your question on
Piazza.
2. Generate association rules based on the templates. The following are templates:
Template 1: {RULE|BODY|HEAD} HAS ({ANY|NUMBER|NONE}) OF
(ITEM1, ITEM2, …, ITEMn)
Template 2: SizeOf({BODY|HEAD|RULE}) ≥ NUMBER.
Template 3: Any combined templates using AND or OR. For example:
HEAD HAS (1) OF (Disease) AND BODY HAS (NONE) OF (Disease)
Below is an example illustrating RULE, BODY and HEAD in the templates:
Assume we obtain a RULE {G1_Up, G3_Down} {G4_Down, G34_Up}.
{G1_Up, G3_Down} is BODY and {G4_Down, G34_Up} is HEAD.
If support = 50% and confidence = 70% are given, you need to generate all the
rules satisfying these requirements. In your report, you are asked to show the
number of rules generated. However, in your code, you need to make sure that
support and confidence can be changed to other values in new queries, and show
and count the resulting rules you generate for each query. Please see Template.pdf
for details.
3. Prepare your submission. Make a folder named Association, in the folder you
should include:
a. Report: A pdf file named as Association_report.pdf. The report should
include:
i. Describe Apriori algorithm and the flow of the association rule
generation algorithm briefly.
ii. The answers of aforementioned queries in required tasks 1&2
(Number of frequent itemsets or generated rules).
b. A folder named Code, which contains all codes used in this part. Inside the
folder, please have a file README which describes how to run your code.
Project Submission:
1. Your final submission should be a zip file named as project1.zip. In the zip file,
you should include aforementioned folder PCA and folder Association.
2. Log in any CSE department server and submit your zip file as follows:
>> submit_cse601 project1.zip
Note that copying code/results/report from another group or source is not allowed and
may result in an F in the grades of all the team members. Academic integrity policy can
be found at http://www.cse.buffalo.edu/shared/policies/academic.php.
CSE 601
