Discriminatively Fuzzy Multi-View K-means Clustering with Local Structure Preserving
Authors: Jun Yin, Shiliang Sun, Lai Wei, Pei Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of DFMKLS is evaluated on benchmark multi-view datasets. It obtains superior performances than state-of-the-art multi-view clustering methods, including multi-view K-means. and Experiments are performed in MATLAB R2014a on a computer with 13th Gen Intel(R) Core(TM) i9-13900K 3.00 GHz CPU, 64.0GB RAM and Windows11 operating system. Accuracy, F-score, Normalized Mutual Information (NMI) and Precision are employed to measure the clustering performance. |
| Researcher Affiliation | Academia | 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China 2Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China |
| Pseudocode | Yes | Algorithm 1: DFMKLS algorithm |
| Open Source Code | No | The paper does not contain an explicit statement about the availability of the source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | Experiments are conducted on 3sources, BBC, Web KB and NUS WIDE datasets. Table 1 summarizes four datasets, and their details are presented as follows. (1) 3Sources (Greene and Cunningham 2009): ... (2) BBC (Greene and Cunningham 2006): ... (3) Web KB (Craven et al. 2000): ... (4) NUS WIDE (Chua et al. 2009): |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | Experiments are performed in MATLAB R2014a on a computer with 13th Gen Intel(R) Core(TM) i9-13900K 3.00 GHz CPU, 64.0GB RAM and Windows11 operating system. |
| Software Dependencies | Yes | Experiments are performed in MATLAB R2014a |
| Experiment Setup | Yes | For DFMKLS, the trade-off parameter α is set as 0.01, and the neighbor parameter K is set as 10. Parameter setting will be analyzed in the following section. |