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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Discriminatively Fuzzy Multi-View K-means Clustering with Local Structure Preserving
Authors: Jun Yin, Shiliang Sun, Lai Wei, Pei Wang
AAAI 2024 | Venue PDF | 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. |