k-anonymity.cpp: Implements a sampling-based acceleration of the clustering algorithm proposed in [ZWL+18]. 
To improve performance, the algorithm selects new cluster centers from a random sample of the remaining data points, rather than the entire dataset.
Additionally, the code has been optimized with assistance from a language model to enhance structure, and execution speed (e.g., multi-threading where applicable and pre-processing steps to speed up pairwise distance computations).

To run the code, you first need to set the correct path to load the dataset and configure DIM according to the dataset’s dimensionality. The data is assumed to be sparse and one-hot encoded (as provided in the supplementary materials).