GitHub - maheshakya/DistClust_via_LSH_L2: This repository contains the experiments conducted in the paper: "Distributed Clustering via LSH Based Data Partitioning" (ICML 2018) with synthetic data. This implementation is not a distributed implementation. It is a single machine implementation intended to demonstrate the properties of this technique and approximation results.

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This repository contains the experiments conducted in the paper: "Distributed Clustering via LSH Based Data Partitioning" (ICML 2018) with synthetic data. This implementation is not a distributed implementation. It is a single machine implementation intended to demonstrate the properties of this technique and approximation results.

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
lsh_functions.py		lsh_functions.py
n_hashes_vs_counts_experiments.py		n_hashes_vs_counts_experiments.py
plsh_experiments.py		plsh_experiments.py
plsh_vs_kmeans_comp.py		plsh_vs_kmeans_comp.py
readme.txt		readme.txt
synthetic_data_gen.py		synthetic_data_gen.py

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Need:
	- Python 2.7
	- numpy
	- scikit-learn
	- matplotlib

Commands:

For properties of LSH based clustering, run: python n_hashes_vs_counts_experiments.py
For comparing approximation results with k-means++, run: python plsh_vs_kmeans_comp.py
For other experiments, run: python plsh_experiments.py