Analysis and Study of Incremental DBSCAN Clustering Algorithm

In this paper, the authors describe the incremental behaviors of density based clustering. It specially focuses on the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and its incremental approach. DBSCAN relies on a density based notion of clusters. It discovers clusters of arbitrary shapes in spatial databases with noise. In incremental approach, the DBSCAN algorithm is applied to a dynamic database where the data may be frequently updated. After insertions or deletions to the dynamic database, the clustering discovered by DBSCAN has to be updated.

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Cornell University
Data Management