Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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 and potential bias; scores should be interpreted as estimates.
Multiple Partitions Aligned Clustering
Authors: Zhao Kang, Zipeng Guo, Shudong Huang, Siying Wang, Wenyu Chen, Yuanzhang Su, Zenglin Xu
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,088 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,395 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on four benchmark data sets: BBCSport, Caltech7, Caltech20, Handwritten Numerals. Their statistics information is summarized in Table 1. We compare the proposed m PAC with several state-of-the-art methods from different categories |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, University of Electronic Science and Technology of China 2School of Foreign Languages, University of Electronic Science and Technology of China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Optimization for m PAC |
| Open Source Code | Yes | Our code is available: https://github.com/sckangz/mPAC |
| Open Datasets | Yes | We conduct experiments on four benchmark data sets: BBCSport, Caltech7, Caltech20, Handwritten Numerals. Their statistics information is summarized in Table 1. |
| Dataset Splits | No | The paper mentions conducting experiments on benchmark datasets and repeating methods 10 times but does not provide specific training, validation, or test dataset split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | Input: Multiview matrix X1, , Xv, cluster number c, parameters α, β, γ. ... Taking Caltech7 as an example, we demonstrate the influence of parameters to clustering performance. ... it becomes more robust to α and β when γ increases, which indicates the importance of partition alignment. |