Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Dynamic Anchor-based Ensemble Clustering via Hypergraph Reconstruction
Authors: Jiaxuan Xu, Lei Duan, Xinye Wang, Liang Du
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the outstanding performance of the proposed YACHT. The code is available at https://github.com/scu-kdde/YACHT. We conduct extensive experiments on 10 real datasets. Characteristics of these datasets are provided in Table 1. |
| Researcher Affiliation | Academia | 1School of Computer Science, Sichuan University, Chengdu, China 2School of Computer and Information Technology, Shanxi University, Taiyuan, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Obatain label indicator matrix F; Algorithm 2 YACHT |
| Open Source Code | Yes | The code is available at https://github.com/scu-kdde/YACHT. |
| Open Datasets | Yes | We conduct extensive experiments on 10 real datasets. Characteristics of these datasets are provided in Table 1. Some methods [Zhou et al., 2019; Tao et al., 2019] provide relevant base clustering results, which we use directly for convenience. For the other datasets1, we randomly run the K-means method 100 times to generate the base clustering result set. 1http://archive.ics.uci.edu/datasets |
| Dataset Splits | No | The paper describes generating base clustering results by running K-means 100 times on the datasets and then evaluates the ensemble clustering performance. It does not provide explicit training/test/validation splits for the datasets themselves but rather uses the full datasets for evaluation in the context of clustering. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used for running the experiments. It mentions 'excessive runtime or insufficient memory' for some methods, implying computational constraints, but no concrete hardware specifications are provided. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In YACHT, we set the hyperedge weight parameter θ = 0.4 and the maximum order of hyperedge random walks d = 20. We set the hyperparameters as follows: the number of anchors k = k +c, where k { 1, 0, 1, 2, 3}; and the similarity threshold α [0.9 : 0.01 : 1]. |