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

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.