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Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion - Position Paper.

M. Hund, M. Behrisch, I. Färber, M. Sedlmair, T. Schreck, T. Seidl, and D. Keim. SISAP, volume 9371 of Lecture Notes in Computer Science, page 307-313. Springer, (2015)

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Dr. Ines Böhner University of Stuttgart

English for Science and Technology (C1)I. Böhner, G. Chojnacki-Herbers, J. Michaels, and J. Nixon. Carl Hanser Verlag GmbH & Co. KG, München, 1 edition, (2024)

Ines Waldsteiner University of Stuttgart

Ines Lombardi University of Stuttgart

Ines Hofmann University of Stuttgart

Ines Schalk University of Stuttgart

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Mining orthogonaler Konzepte in hochdimensionalen Datenbanken.I. Färber. Informatiktage, volume S-8 of LNI, page 153-156. GI, (2009)Spectral Subspace Clustering for Graphs with Feature Vectors.S. Günnemann, I. Färber, S. Raubach, and T. Seidl. ICDM, page 231-240. IEEE Computer Society, (2013)MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions.S. Günnemann, H. Kremer, I. Färber, and T. Seidl. ICDM Workshops, page 1387-1390. IEEE Computer Society, (2010)Subspace search and visualization to make sense of alternative clusterings in high-dimensional data.A. Tatu, F. Maass, I. Färber, E. Bertini, T. Schreck, T. Seidl, and D. Keim. IEEE VAST, page 63-72. IEEE Computer Society, (2012)Detection of orthogonal concepts in subspaces of high dimensional data.S. Günnemann, E. Müller, I. Färber, and T. Seidl. CIKM, page 1317-1326. ACM, (2009)External evaluation measures for subspace clustering.S. Günnemann, I. Färber, E. Müller, I. Assent, and T. Seidl. CIKM, page 1363-1372. ACM, (2011)Filtertechniken für geschützte biometrische Datenbanken.C. Böhm, I. Färber, S. Fries, U. Korte, J. Merkle, A. Oswald, T. Seidl, B. Wackersreuther, and P. Wackersreuther. BTW, volume P-180 of LNI, page 379-389. GI, (2011)Visual Quality Assessment of Subspace ClusteringsM. Hund, I. Färber, M. Behrisch, A. Tatu, T. Schreck, D. Keim, and T. Seidl. Proceedings of the KDD Workshop on Interactive Data Exploration and Analytics (IDEA), page 53-62. (2016)CoDA: Interactive Cluster Based Concept Discovery.S. Günnemann, I. Färber, H. Kremer, and T. Seidl. PVLDB, 3 (2): 1633-1636 (2010)Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion - Position Paper.M. Hund, M. Behrisch, I. Färber, M. Sedlmair, T. Schreck, T. Seidl, and D. Keim. SISAP, volume 9371 of Lecture Notes in Computer Science, page 307-313. Springer, (2015)