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Best Paper Awards at SISAP and LWDA

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Auf dem Bild stehen die Gewinner der Awards - v. l. Erik Thordsen, Prof. Dr. Erich Schubert, Lars Lenssen, Niklas Strahmann © @Dept. Computer Science VIII
v. l. Erik Thordsen, Prof. Dr. Erich Schubert, Lars Lenssen, Niklas Strahmann
The data mining group of Prof. Schubert has received three awards in one week

For improving Sketch-based similarity search in high-dimensional data, both the "best student paper award" and the "best paper award" at the 16th International Conference on Similarity Search and Applications, SISAP in A Coruña.

  • Erik Thordsen and Erich Schubert. An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search. Int. Conf. Similarity Search and Applications, SISAP. 2023. doi:10.1007/978-3-031-46994-7_4. to appear.

The paper is also invited to be submitted as a long version to a Special Issue of the Journal Information Systems (IS).

Accelerating nearest-neighbor-consistent k-means clustering received the "best paper award" from the GI Knowledge Discovery, Data Mining, and Machine Learning (KDML) Division at "Learning, Knowledge, Data, Analytics" (LWDA).

  • Lars Lenssen, Niklas Strahmann and Erich Schubert. Fast k-Nearest-Neighbor-Consistent Clustering. Learning, knowledge, data, analytics. 2023. to appear.

Further information:

First paper

SISAP

LWDA