Clustering in the Presence of Background Noise
Authors: Shai Ben-David, Nika Haghtalab
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 8, we further demonstrate the gap between these two paradigms and use experiments to confirm our theoretical results. |
| Researcher Affiliation | Academia | David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1 CANADA; Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses synthetically generated data and does not provide access information for a publicly available dataset: 'For any k, we use n = 50000 data points on the unit square. 90% of the points come from k Gaussian distributions with centers selected uniformly at random and standard deviation = 1/n. Additionally, 10% uniform noise is introduced in the data.' |
| Dataset Splits | No | The paper describes the synthetic data generation but does not provide specific dataset split information (e.g., percentages, sample counts, or explicit validation splits). |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or detailed computer specifications) used for running experiments were found. |
| Software Dependencies | No | The paper mentions algorithms like Lloyd but does not provide specific software names with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | For δ-k-means, we adapt the Lloyd algorithm to calculate the clustering using a δ-truncated distance matrix in every iteration, where δ is set to 10. |