Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?

By Michael E. Houle, Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek. International Conference on Statistical and Scientific Database Management, 2010

305 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/87937e1f08d7310b2024a7bcd9db9f5ce4fc60cb

How researchers see this paper

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

1. Ricardo José Gabrielli Barreto Campello
2. Erich Schubert
3. Jörg Sander
4. Hans-Peter Kriegel
5. Charu C. Aggarwal
6. Arthur Zimek
7. Martin Ester
8. Michael Hahsler

Citations in the corpus

• Density‐based clustering (2019, background): “This claim has been experimentally evaluated by Houle et al. (2010).”
• Density‐based clustering (2011, background): “The challenge of finding density-based clusters with very different densities in a data set, which cannot be completely described by a single density level set, has been addressed, e.g., by Refs 25, 26 for point data.”
• DBSCAN Revisited, Revisited (2017, background): “Independent of the algorithm, the parameter ε of DBSCAN becomes hard to choose in highdimensional data due to the loss of contrast in distances [9, 25, 48].”