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https://github.com/Galaxies99/EE359-Labs
The Labs of EE359: Data Mining
https://github.com/Galaxies99/EE359-Labs
sjtu-courses
Last synced: 6 days ago
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The Labs of EE359: Data Mining
- Host: GitHub
- URL: https://github.com/Galaxies99/EE359-Labs
- Owner: Galaxies99
- License: mit
- Created: 2021-03-18T01:44:24.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-06-12T15:12:43.000Z (over 3 years ago)
- Last Synced: 2024-06-11T10:45:48.932Z (5 months ago)
- Topics: sjtu-courses
- Language: Jupyter Notebook
- Homepage:
- Size: 162 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-cs - @Galaxies99, 2021 Spring
README
# EE359-Labs
This is a repository for labs of EE359: Data Mining Techniques in Shanghai Jiao Tong University. Currently, two labs are completed, which are
- Lab 1: clustering
- Lab 2: community detection
- Lab 3: link prediction
## Lab 1: Clustering
Kaggle competition is available at: https://www.kaggle.com/c/datamining-2021.
We mainly implement K-Means Algorithm in the clustering lab.
References:
1. Lloyd, Stuart. *Least squares quantization in PCM*. Initially published as *Bell Telephone Laboratories Paper* (1958), and later published in journal *IEEE transactions on information theory* 28.2 (1982): 129-137.
2. Arthur, David, and Sergei Vassilvitskii. *k-means++: The advantages of careful seeding*. Stanford, 2006.
3. Ayoosh Kathuria. *Speed Up K-Means Clustering by 70x*. Online, 2020. Available: https://blog.paperspace.com/speed-up-kmeans-numpy-vectorization-broadcasting-profiling/
## Lab 2: Community Detection
Kaggle competition is available at: https://www.kaggle.com/c/datamining-2021-2.
We mainly implement Louvain Algorithm and Leiden Algorithm in the community detection lab.
References:
1. Traag, Vincent A., Ludo Waltman, and Nees Jan Van Eck. "From Louvain to Leiden: guaranteeing well-connected communities." Scientific reports 9.1 (2019): 1-12.
2. Blondel, Vincent D., et al. "Fast unfolding of communities in large networks." Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.
## Lab 3: Link Prediction
We mainly implement node2vec algorithm in the link prediction lab.
Referecnces:
1. Li, Aaron Q., et al. "Reducing the sampling complexity of topic models.", Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014.
2. Grover, Aditya, and Jure Leskovec. "node2vec: Scalable feature learning for networks." Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016.
## Notes
All work of the repository is done by Galaxies, any form of plagiarization is strictly forbidden. But, feel free to use the implementations if you find them useful.
```bibtex
@misc{fang2021kmeans_ee359,
author = {Hongjie Fang},
title = {KMeans Algorithm Implementation in EE359 Labs of SJTU},
howpublished = {\url{https://github.com/Galaxies99/EE359-Labs/tree/main/clustering}},
year = {2021}
}
```
```bibtex
@misc{fang2021louvain_ee359,
author = {Hongjie Fang},
title = {Louvain Algorithm Implementation in EE359 Labs of SJTU},
howpublished = {\url{https://github.com/Galaxies99/EE359-Labs/tree/main/community}},
year = {2021}
}
```
```bibtex
@misc{fang2021leiden_ee359,
author = {Hongjie Fang},
title = {Leiden Algorithm Implementation in EE359 Labs of SJTU},
howpublished = {\url{https://github.com/Galaxies99/EE359-Labs/tree/main/community}},
year = {2021}
}
```
```bibtex
@misc{fang2021node2vec_ee359,
author = {Hongjie Fang},
title = {Node2Vec Algorithm Implementation in EE359 Labs of SJTU},
howpublished = {\url{https://github.com/Galaxies99/EE359-Labs/tree/main/linkprediction}},
year = {2021}
}
```