Granular-Ball-Induced Multiple Kernel K-Means
Authors: Shuyin Xia, Yifan Wang, Lifeng Shen, Guoyin Wang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using granular-ball relationships in multiple kernel spaces, the proposed GB-MKKM framework shows its superiority in efficiency and clustering performance in the empirical evaluation of various clustering tasks. |
| Researcher Affiliation | Academia | 1 Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, and the Key Laboratory of Big Data Intelligent Computing, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications 2 National Center for Applied Mathematics, Chongqing Normal University |
| Pseudocode | Yes | Algorithm 1 GB-SMKKM Algorithm |
| Open Source Code | Yes | Source code and supplementary materials are available at: https: //github.com/Wang Yifan4211115/GB-MKKM. |
| Open Datasets | Yes | We evaluated the proposed algorithm on 12 datasets. The detailed information of datasets is summarized in Table 1. As can be seen, these datasets differ significantly in terms of sample size, multi-view attributes, dimensionality, and the number of categories, making them highly representative and capable of comprehensively verifying the applicability and robustness of the algorithm. |
| Dataset Splits | No | The paper lists various datasets and their characteristics in Table 1 but does not explicitly provide information regarding specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper states, "The experiments are conducted using MATLAB R2022a" but does not provide any specific details about the hardware used, such as GPU or CPU models. |
| Software Dependencies | Yes | The experiments are conducted using MATLAB R2022a. |
| Experiment Setup | Yes | For a fair comparison, we uniformly employ three types of kernels (with various parameters): Linear, Polynomial, and Gaussian, resulting in 6 kernel functions. It is noteworthy that for the comparison with RMKKM, we adopt the optimal parameters and 12 kernel functions as used in their original paper [Du et al., 2015]. |