Conference Proceedings

Dynamic Density Based Clustering

Junhao Gan, Yufei Tao

Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD 2017), Chicago, IL, USA, May 14-19, 2017 | Association for Computing Machinery (ACM) | Published : 2017

Abstract

Dynamic clustering-how to efficiently maintain data clusters along with updates in the underlying dataset-is a difficult topic. This is especially true for density-based clustering, where objects are aggregated based on transitivity of proximity, under which deciding the cluster(s) of an object may require the inspection of numerous other objects. The phenomenon is unfortunate, given the popular usage of this clustering approach in many applications demanding data updates. Motivated by the above, we investigate the algorithmic principles for dynamic clustering by DBSCAN, a successful representative of density-based clustering, and ρ-approximate DBSCAN, proposed to bring down the computationa..

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