This is the official repository for the ICML-2024 paper "MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations".
MC-GTA is a model-based, multivariate clustering algorithm that considers complex metric constraints. It clusters observations that are both similar in terms of features and close in terms of metric distance. A wide range of realistic problems fall into this category, e.g. clustering of time series, spatially distributed POIs, or point clouds.
MC-GTA formulates the clustering objective as minimizing the penalized sum of goodness-of-fit test statistics. A pair of intra-cluster observations is expected to pass the goodness-of-fit test specified by the Wasserstein-2 distance between their underlying models. The criterion of passing such tests is a function of metric distance, i.e., our proposed generalized model-based semivariogram.
Run cluster.py. It automatically loads the Pavement dataset with 1055 10-dimensional temporal observations from the data/pavement/ folder.
For tutorial purpose, we use the first 5 dimensions of the Pavement data and MC-GTA will cluster the temporal sequences into clusters. All intermediate results of time-consuming computations (such as underlying model fitting, semivariogram fitting) are archived in the checkpoints/ folder.
After archiving the data, we show how to do very swift hyperparameter tuning by grid search. Set run_from_archive to True to avoid duplicate intermediate computations. Here for demo purpose only, we split 200 random observations as validation set and tune the hyperparameters against it; after tuning, we apply the hyperparameters to cluster the rest of the dataset and achieve comparable performance. For real applications, you need a separate, independently labeled validation set and repeat the process above.
The tuned hyperparameters achieve 92.76 ARI and 87.99 NMI on the validation set; and on the rest of the dataset, the same hyperparameters achieve 91.93 ARI and 88.33 NMI.
For any questions, please feel free to contact Zhangyu Wang (zhangyuwang@ucsb.edu).
If you find MC-GTA beneficial for your research, please consider citing us with the following BibTeX entry:
@misc{wang2024mcgta,
title={MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations},
author={Zhangyu Wang and Gengchen Mai and Krzysztof Janowicz and Ni Lao},
year={2024},
eprint={2405.18395},
archivePrefix={arXiv},
primaryClass={cs.LG}
}