DOI: 10.5281/zenodo.15530593
title: FastLloyd Clustering Datasets

description: This artifact bundles the five dataset archives used in our private federated clustering evaluation, corresponding to the real-world benchmarks, scaling experiments, ablation studies, and timing performance tests described in the paper. The real_datasets.tar.xz includes ten established clustering benchmarks drawn from UCI and the Clustering basic benchmark (DOI: https://doi.org/10.1007/s10489-018-1238-7); scale_datasets.tar.xz contains the SynthNew family generated to assess scalability via the R clusterGeneration package  ; ablate_datasets.tar.xz holds the AblateSynth sets varying cluster separation for ablation analysis also powered by clusterGeneration  ; g2_datasets.tar.xz packages the G2 sets—Gaussian clusters of size 2048 across dimensions 2–1024 with two clusters each, collected from the Clustering basic benchmark (DOI: https://doi.org/10.1007/s10489-018-1238-7) ; and timing_datasets.tar.xz includes the real s1 and lsun datasets alongside TimeSynth files (balanced synthetic clusters for timing), as per Mohassel et al.’s experimental framework  .

Contents

1. real_datasets.tar.xz

Contains ten real-world benchmark datasets and formatted as one sample per line with space-separated features:





iris.txt: 150 samples, 4 features, 3 classes; classic UCI Iris dataset for petal/sepal measurements.




lsun.txt: 400 samples, 2 features, 3 clusters; two-dimensional variant of the LSUN dataset for clustering experiments  .




s1.txt: 5,000 samples, 2 features, 15 clusters; synthetic benchmark from Fränti’s S1 series.




house.txt: 1,837 samples, 3 features, 3 clusters; housing data transformed for clustering tasks.




adult.txt: 48,842 samples, 6 features, 3 clusters; UCI Census Income (“Adult”) dataset for income bracket prediction.




wine.txt: 178 samples, 13 features, 3 cultivars; UCI Wine dataset with chemical analysis features.




breast.txt: 569 samples, 9 features, 2 classes; Wisconsin Diagnostic Breast Cancer dataset.




yeast.txt: 1,484 samples, 8 features, 10 localization sites; yeast protein localization data.




mnist.txt: 10,000 samples, 784 features (28×28 pixels), 10 digit classes; MNIST handwritten digits.




birch2.txt: (a random) 25,000/100,000 subset of samples, 2 features, 100 clusters; synthetic BIRCH2 dataset for high-cluster‐count evaluation .



2. scale_datasets.tar.xz

Holds the SynthNew_{k}_{d}_{s}.txt files for scaling experiments, where:





$k \in \{2,4,8,16,32\}$ is the number of clusters,




$d \in \{2,4,8,16,32,64,128,256,512\}$ is the dimensionality,




$s \in \{1,2,3\}$ are different random seeds.



These are generated with the R clusterGeneration package with cluster sizes following a $1:2:...:k$ ratio. We incorporate a random number (in $[0, 100]$) of randomly sampled outliers and set the cluster separation degrees randomly in $[0.16, 0.26]$, spanning partially overlapping to separated clusters.

3. ablate_datasets.tar.xz

Contains the AblateSynth_{k}_{d}_{sep}.txt files for ablation studies, with:





$k \in \{2,4,8,16\}$ clusters,




$d \in \{2,4,8,16\}$ dimensions,




$sep \in \{0.25, 0.5, 0.75\}$ controlling cluster separation degrees.



Also generated via clusterGeneration.

4. g2_datasets.tar.xz

Packages the G2 synthetic sets (g2-{dim}-{var}.txt) from the clustering-data benchmarks:





$N=2048$ samples, $k=2$ Gaussian clusters,




Dimensions $d \in \{1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024\}$


Cluster overlap $var \in \{10, 20, 30, 40, 50, 60, 70, 80, 90, 100\}$


5. timing_datasets.tar.xz

Includes:





s1.txt, lsun.txt: two real datasets for baseline timing.




timesynth_{k}_{d}_{n}.txt: synthetic timing datasets with balanced cluster sizes C_{avg}=N/K, varying:





$k \in \{2,5\}$




$d \in \{2,5\}$




$N \in \{10000; 100000\}$





Generated similarly to the scaling sets, following Mohassel et al.’s timing experiment protocol  .

Usage:

Unpack any archive with tar -xJf <archive>.tar.xz to access the .txt files directly for replication of clustering experiments. Each file contains one data point per line, with features separated by spaces.