Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Authors: Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M Procopiuc, Claudio Gentile
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, since our algorithms are principled but also very practical, we carry out an experimental comparison on both synthetic and real-world datasets showing competitive results against known baselines. |
| Researcher Affiliation | Collaboration | 1Google Research, NY, USA 2Lille University and INRIA Lille, France 3Tel-Aviv University, Israel. |
| Pseudocode | Yes | The general pseudocode for Insµ is given in Appendix A. In the following sections, we specify particular measures µ from which hyperplanes can be efficiently sampled and which additionally give rise to HC algorithms having the sequential property, and exhibiting good approximation ratios for the metrics of Section 2. The associated insertion operations are presented in the corresponding sections of the appendix. |
| Open Source Code | No | The paper does not provide any statements about open-sourcing its code or links to a code repository. |
| Open Datasets | Yes | For real-world datasets, we compare the algorithms on the following data of varying scale: MNIST, ALOI (Geusebroek et al. (2005)), and ILSVRC12 (Deng et al. (2009)) trained with Res Net34 architecture. |
| Dataset Splits | No | The paper describes generating synthetic data (10K examples from standard Gaussians) and sampling triplets for evaluation (10K triplets), but it does not specify explicit training, validation, or test dataset splits in terms of percentages, sample counts, or predefined splits for model development/evaluation in a traditional sense. |
| Hardware Specification | No | For hardware, we used machines with a maximum of 125GB of RAM and 16 CPUs. This provides general information but lacks specific models (e.g., CPU model, GPU model if any were used) or precise memory amounts, which are required for full reproducibility. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific solvers). |
| Experiment Setup | Yes | We choose σ as the mean ℓ2 distance between pairs of points. This is to ensure a reasonable distribution of similarity weights. |