Robust and Sparse Fuzzy K-Means Clustering
Authors: Jinglin Xu, Junwei Han, Kai Xiong, Feiping Nie
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark datasets demonstrate that the proposed algorithm not only can ensure the robustness of such soft clustering algorithm in real world applications, but also can avoid the performance degradation by considering the membership sparsity. |
| Researcher Affiliation | Academia | Northwestern Polytechnical University, Xi an, 71072, P. R. China |
| Pseudocode | Yes | Algorithm 1 The algorithm of RSFKM method |
| Open Source Code | No | The paper does not provide any links to or statements about the availability of its source code. |
| Open Datasets | Yes | Among those, two datasets are image datasets, COIL-201 and COIL-1002. The rest is the MNIST3 database of handwritten digits. 1http://www.cs.columbia.edu/CAVE/software/softlib/coil20.php 2http://www.cs.columbia.edu/CAVE/software/softlib/coil100.php 3http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | No | The paper mentions evaluating on benchmark datasets and repeating clustering 10 times, but it does not explicitly provide details about specific training, validation, or test splits (e.g., percentages or sample counts) used for these datasets. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific libraries). |
| Experiment Setup | Yes | For the regularization parameter γ, it puts a restriction on the minimum distance between a data point and a cluster center and prevents membership from having extreme values, 0 and 1. ... In this paper, the optimal value of γ was set empirically using the grid search method in a range from [10 1, 101] every 0.5 step. For the threshold value ", it mainly controls the number of outliers... Here we select " in a range of [0, 3]. |