A Robust Convex Formulation for Ensemble Clustering
Authors: Junning Gao, Makoto Yamada, Samuel Kaski, Hiroshi Mamitsuka, Shanfeng Zhu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first showed that using synthetic data experiments, RCEC could learn stable cluster assignments from the input matrix including anomalous clusters. We then showed that RCEC outperformed state-of-the-art ensemble clustering methods by using real-world data sets. |
| Researcher Affiliation | Academia | Junning Gao,1 Makoto Yamada,2 Samuel Kaski,2,3 Hiroshi Mamitsuka,2,3 Shanfeng Zhu1 1 School of Computer Science and Shanghai Key Lab of Intelligent Information Processing Fudan University, Shanghai, China. 2 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan. 3 Department of Computer Science, Aalto University, Finland. |
| Pseudocode | Yes | Algorithm 1 The RCEC algorithm |
| Open Source Code | No | No concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper was provided. |
| Open Datasets | Yes | Tr11 [Karypis, 2002], K1b [Karypis, 2002], ORL [Cai et al., 2006] |
| Dataset Splits | No | The paper describes varying input feature ratios and repetitions of experiments, but does not specify clear train/validation/test dataset splits needed for reproduction. It mentions "randomly chose 60%, 70%, . . . , 100% of the entire features for experiments" and "repeated the experiment 10 times by changing the random seed" but this is not a dataset split. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments are provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided. |
| Experiment Setup | Yes | We used λ = 0.1, γ = 0.01, and β = {0.01, 1, 2, 4, 6, . . . , 20}. |