{"id":18797631,"url":"https://github.com/lirongwu/dcv","last_synced_at":"2025-04-13T16:32:14.160Z","repository":{"id":52273581,"uuid":"459152099","full_name":"LirongWu/DCV","owner":"LirongWu","description":"Code for TNNLS paper \"Deep Clustering and Visualization for End-to-End High Dimensional Data analysis\"","archived":false,"fork":false,"pushed_at":"2022-12-16T04:23:41.000Z","size":13,"stargazers_count":9,"open_issues_count":0,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-27T07:48:11.056Z","etag":null,"topics":["clustering","geometric-deep-learning","high-dimensional-data","manifold-learning","visualization"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LirongWu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-02-14T12:25:25.000Z","updated_at":"2025-03-03T10:25:31.000Z","dependencies_parsed_at":"2023-01-29T08:15:15.734Z","dependency_job_id":null,"html_url":"https://github.com/LirongWu/DCV","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LirongWu%2FDCV","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LirongWu%2FDCV/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LirongWu%2FDCV/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LirongWu%2FDCV/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LirongWu","download_url":"https://codeload.github.com/LirongWu/DCV/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248743914,"owners_count":21154767,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["clustering","geometric-deep-learning","high-dimensional-data","manifold-learning","visualization"],"created_at":"2024-11-07T22:08:57.632Z","updated_at":"2025-04-13T16:32:13.918Z","avatar_url":"https://github.com/LirongWu.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep Clustering and Visualization (DCV)\n\n\nThis is a PyTorch implementation of the DCV, and the code includes the following modules:\n\n* Datasets (MNIST, HAR, USPS, Pendigits, Reuters-10K, Coil100)\n\n* Training for DCV-encoder and DCV-decoder\n\n* Visualization\n\n* Evaluation metrics \n\n  \n\n## Requirements\n\n* pytorch == 1.6.0\n\n* scipy == 1.3.1\n\n* numpy == 1.18.5\n\n* scikit-learn == 0.21.3\n\n* umap == 1.18.5\n\n* networkx == 2.3\n\n  \n\n## Description\n\n* main.py  \n  * Train() -- Train a new model\n  * Test() -- Test the learned model for evaluating generalization\n* dataloader.py  \n  \n  * GetData() -- Load data of selected dataset\n* model.py  \n  \n  * LISV2_MLP() -- model and loss\n* tool.py  \n  * GIFPloter() -- Auxiliary tool for online plot\n  \n  * DataSaver() -- Save intermediate and final results\n  \n  * cluster_acc() -- Calculate clustering accuracy\n  \n\n\n\n## Dataset\n\nThe datasets and pretrained models used in this paper are available in:\n\nhttps://drive.google.com/file/d/19oO9l9WgnPZuqojKFVtwIRFm4s0vcY02/view?usp=sharing\n\n\n\n## Running the code\n\n1. Install the required dependency packages\n2. To get the results on a specific *dataset*, run with proper hyperparameters\n\n  ```\npython main.py --data_name dataset\n  ```\n\n3. To get the data, metrics, and visualisation, refer to\n\n  ```\n../log/dataset/\n  ```\n\nwhere the *dataset* is one of the six datasets (MNIST, HAR, USPS, Pendigits, Reuters-10K, Coil100)\n\n\n\n## Citation\n\nIf you find this project useful for your research, please use the following BibTeX entry.\n\n```\n@article{wu2022deep,\n  title={Deep Clustering and Visualization for End-to-End High-Dimensional Data Analysis},\n  author={Wu, Lirong and Yuan, Lifan and Zhao, Guojiang and Lin, Haitao and Li, Stan Z},\n  journal={IEEE Transactions on Neural Networks and Learning Systems},\n  year={2022},\n  publisher={IEEE}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flirongwu%2Fdcv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flirongwu%2Fdcv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flirongwu%2Fdcv/lists"}