Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)

Jeong-Hun Kim, Tserenpurev Chuluunsaikhan, Jong-Hyeok Choi, Aziz Nasridinov

AAAI 2023 pp. 16242-16243

doi:10.1609/AAAI.V37I13.26981 /aaai/2023/kim2023aaai-incremental/

Abstract

DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch to update clusters when new data is inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates clusters by identifying in advance regions where cluster updates will occur. Also, through extensive experiments, we show that our method provides clustering results similar to those of DBSCAN.

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Text

Kim et al. "Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26981

Markdown

[Kim et al. "Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/kim2023aaai-incremental/) doi:10.1609/AAAI.V37I13.26981

BibTeX

@inproceedings{kim2023aaai-incremental,
  title     = {{Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)}},
  author    = {Kim, Jeong-Hun and Chuluunsaikhan, Tserenpurev and Choi, Jong-Hyeok and Nasridinov, Aziz},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16242-16243},
  doi       = {10.1609/AAAI.V37I13.26981},
  url       = {https://mlanthology.org/aaai/2023/kim2023aaai-incremental/}
}