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

Authors

  • Jeong-Hun Kim Chungbuk National University
  • Tserenpurev Chuluunsaikhan Chungbuk National University
  • Jong-Hyeok Choi Chungbuk National University
  • Aziz Nasridinov Chungbuk National University

DOI:

https://doi.org/10.1609/aaai.v37i13.26981

Keywords:

Data Mining, Clustering, Incremental Clustering, DBSCAN, Grid Partitioning

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.
AAAI-23 Proceedings Cover

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Published

2024-07-15

How to Cite

Kim, J.-H., Chuluunsaikhan, T., Choi, J.-H., & Nasridinov, A. (2024). Incremental Density-Based Clustering with Grid Partitioning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16242-16243. https://doi.org/10.1609/aaai.v37i13.26981

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program