From Ensemble Clustering to Multi-View Clustering
Authors: Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, Yun Fu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on six realworld datasets show the efficacy of our approach compared with both MVC and EC methods. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA 2College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA |
| Pseudocode | Yes | Algorithm 1. Multi-View Ensemble Clustering by ALM |
| Open Source Code | No | The paper does not include an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Six real-world datasets are used in the experiment, which cover three text-type ones, i.e., the 3-Source1 dataset, the 4-Areas2 dataset, and the BBCSport dataset provided by [Xia et al., 2014]; and three image-type ones, i.e., a 20-class subset [Li et al., 2015b] of the Caltech1013 image dataset, the UCI Digit4 dataset, and the Notting-Hill dataset [Zhang et al., 2009] provided by [Cao et al., 2015]. We summarize these datasets in Table 1. and footnotes: 1http://mlg.ucd.ie/datasets, 2http://web.cs.ucla.edu/~yzsun/data/four_area.zip, 3http://www.vision.caltech.edu/Image Datasets/Caltech101/, 4http://archive.ics.uci.edu/ml/datasets.html |
| Dataset Splits | No | The paper describes generating basic partitions and running methods multiple times for stability, but does not specify explicit train/validation/test dataset splits (e.g., percentages, sample counts, or standard cross-validation setup) for the main datasets used. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU model, GPU model, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using K-means and spectral clustering, and states 'we run the codes provided by authors' for compared methods, but it does not specify any software dependencies with version numbers (e.g., Python, specific libraries, or frameworks with their versions). |
| Experiment Setup | Yes | In the experiment, we set λ1 = 1 and λ2 = 0.01 as the default setting for our MVEC method. and We employ r = 100 basic partitions (BPs) for each view v (denoted by Π(v)) in all the above experiments. |