How to Find a Good Explanation for Clustering?
Authors: Sayan Bandyapadhyay, Fedor Fomin, Petr A Golovach, William Lochet, Nidhi Purohit, Kirill Simonov3904-3912
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our rigorous algorithmic analysis sheds some light on the influence of parameters like the input size, dimension of the data, the number of outliers, the number of clusters, and the approximation ratio, on the computational complexity of explainable clustering. |
| Researcher Affiliation | Academia | Sayan Bandyapadhyay1, Fedor Fomin1, Petr A Golovach1, William Lochet1, Nidhi Purohit1, Kirill Simonov 2 1 Department of Informatics, University of Bergen, Norway 2 Algorithms and Complexity Group, TU Wien, Vienna, Austria |
| Pseudocode | No | The paper describes algorithms verbally and through theoretical analysis, but does not provide structured pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving specific datasets, nor does it provide concrete access information for any dataset. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies or version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |