BICO: BIRCH Meets Coresets for k-Means Clustering

By Hendrik Fichtenberger, Marc Gillé, Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler. 2013

79 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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. Chris Schwiegelshohn
2. Melanie Schmidt
3. Christian Sohler
4. David P. Woodruff
5. Dan Feldman
6. Peilin Zhong
7. Meisam Razaviyayn
8. Maher Nouiehed

In Signal Processing

1. Melanie Schmidt
2. Chris Schwiegelshohn
3. Christian Sohler

Citations in the corpus

• Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “global clustering (macro-clusters) [3], [5], [22], [45], [67], [75].”
• Fair Coresets and Streaming Algorithms for Fair k-Means Clustering (2018, methodology): “Movement-based constructions include the original paper due to Har-Peled and Mazumdar [29] as well as the practically more efficient algorithm BICO [25].”