[Solved] CSE601-Project 2- Clustering Algorithms

Please clearly state the UB Person numbers and UB IT names for all the group members on the cover of the report.

Two gene datasets (cho and iyer) can be found on Piazza. Please check the README file first for a short description of the two datasets.

Complete the following tasks:

1. Implement five clustering algorithms to find clusters of genes that exhibit similar expression profiles: K-means, Hierarchical Agglomerative clustering with Min approach, density-based, mixture model, and spectral clustering. Compare these five methods and discuss their pros and cons.

For each of the above tasks, you are required to validate your clustering results using the following methods:

• Using external index (Rand Index and Jaccard Coefficient) and compare the clustering results from different clustering algorithms. The ground truth clusters are provided in the datasets.
• Visualize data sets and clustering results by Principal Component Analysis (PCA). You can use the PCA you implemented in Project 1 or use any existing implementation or package.

Your final submission should include the following:

• Codes: A folder named Code, that contains five clustering algorithms and a README that shows how to run your code.
• Report: A pdf file named Cluster_report .pdf. Describe your implementation details about all the algorithms. Compare the performance of these approaches using visualization and external index on the two given data sets. State the pros and cons of each algorithm and any findings you get from the experiments.

Project Submission:

1. Your final submission should be a zip file named as zip. In the zip file, you need to include aforementioned folder Code and folder Report.
2. Log in any CSE department server and submit your zip file as follows:

>> submit_cse601 project2.zip

The details about Demo will be released two days before the demo date through Piazza. Note:

• New data sets will be given to check your implemented clustering algorithms and validation measures. The data format is the same with the project data sets we provide.
• During the demo, the specific parameter setting will be changed and therefore you need to keep your code flexible to different parameters.