Neural Capacitated Clustering
Authors: Jonas K. Falkner, Lars Schmidt-Thieme
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments on artificial data and two real world datasets our approach outperforms several state-of-the-art mathematical and heuristic solvers from the literature. |
| Researcher Affiliation | Academia | Jonas K. Falkner and Lars Schmidt-Thieme Institute of Computer Science, University of Hildesheim, Hildesheim, Germany {falkner, schmidt-thieme}@ismll.uni-hildesheim.de |
| Pseudocode | Yes | Algorithm 1: Capacitated k-means; Algorithm 2: Neural Capacitated Clustering (NCC) |
| Open Source Code | Yes | We open source our code at https://github.com/jokofa/NCC |
| Open Datasets | Yes | As real world datasets for capacitated spatial clustering we select the well-known Shanghai Telecom (ST) dataset [Wang et al., 2019]... The second dataset we assemble by matching the internet access sessions in the call record data of the Telecom Italia Milan (TIM) dataset [Barlacchi et al., 2015] with the Milan cell-tower grid retrieved from Open Celli D [OCID, 2021]... To evaluate our algorithm we choose the benchmark dataset of [Uchoa et al., 2017] |
| Dataset Splits | No | The paper mentions tuning parameters on a 'separate validation set' and discusses 'validation accuracy', but does not provide specific details on the size, percentage, or methodology of this validation split from the dataset. |
| Hardware Specification | Yes | All experiments are run on an i7-7700K CPU (4.20GHz). |
| Software Dependencies | Yes | We implement our model and the simple baselines in PyTorch [Paszke et al., 2019] version 1.11 and use Gurobi version 9.1.2 for all methods that require it. |
| Experiment Setup | Yes | We use L = 4 GNN layers, an embedding dimension of demb = 256 and a dimension of dh = 256 for all hidden layers... Then we do supervised training using binary cross entropy (BCE)... The fraction α of final nodes for which the re-prioritization is applied we treat as a hyperparameter... we set reasonable total run times of 3min for n = 200 and 15min for the original sizes. |