Incremental OPTICS: Efficient Computation of Updates in a Hierarchical Cluster Ordering

By Hans-Peter Kriegel, Peer Kröger, Irina Gotlibovich. 2003

37 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tyler L. Hayes
2. Christopher Kanan
3. Nathan D. Cahill
4. Vincenzo Lomonaco
5. Nikos Mamoulis
6. Spiridon Bakiras
7. Panos Kalnis
8. Kushal Kafle

In Computer Networks and Communications

1. Nikos Mamoulis
2. Spiridon Bakiras
3. Panos Kalnis
4. Tyler L. Hayes
5. Christopher Kanan

In Signal Processing

1. Nikos Mamoulis
2. Nathan D. Cahill
3. Spiridon Bakiras
4. Panos Kalnis
5. Tyler L. Hayes
6. Christopher Kanan
7. Anuj Karpatne
8. Gowtham Atluri

Citations in the corpus

• Memory Efficient Experience Replay for Streaming Learning (2018, background): “Density-based algorithms cluster data that lie in high-density regions of feature space together, while labeling points in low-density regions as outliers [9], [11], [16], [30], [48].”
• On Discovering Moving Clusters in Spatio-temporal Data (2005, methodology): “OPTICS [11] is another density based method.”