Commit 3b811226 authored by He Guanlin's avatar He Guanlin
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Update README.md

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......@@ -15,7 +15,9 @@ The configuration for benchmark dataset, block size, etc., are adjustable in the
Our k-means code does NOT generate any synthetic data, so your need to give the path and filename of your benchmark dataset in the `INPUT_DATA` constant, and also specifiy the `NbPoints`, `NbDims`, `NbClusters`. If you want to impose initial centroids, you need to provide a text file and specifiy the corresponding path and filename in the `INPUT_INITIAL_CENTROIDS` constant.
The synthetic dataset used in our papers below is too large (about 1.8GB) to be loaded here. So we provide the _Synthetic_Data_Generator.py_ instead. Since the generator uses the random function, the dataset generated each time will have different values but will always keep the same distribution.
## Benchmark datasets
We tested our code on one synthetic dataset and two real-world datasets. Each of them contains millions of instances, and therefore is too large to be loaded here.
The synthetic dataset used in our paper below is too large (about 1.8GB) to be loaded here. So we provide the _Synthetic_Data_Generator.py_ instead. Since the generator uses the random function, the dataset generated each time will have different values but will always keep the same distribution.
## Execution
Before execution, recompile the code by entering the `make` command if any change has been made to the code.
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./kmeans
```
## Corresponding papers
## Corresponding paper
The approaches and experiments are documented in the following paper.
He, G., Vialle, S., & Baboulin, M. (2021). Parallelization of the k-means algorithm in a spectral clustering chain on CPU-GPU platforms. In B. B. et al. (Ed.), Euro-par 2020: Parallel processing workshops (Vol. 12480, LNCS, pp. 135–147). Warsaw, Poland: Springer. Available from: https://link.springer.com/chapter/10.1007/978-3-030-71593-9_11
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