Analyzing high-dimensional data by subspace validity

Amihood Amir, Reuven Kashi, Nathan S. Netanyahu, Daniel Keim, Markus Wawryniuk

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We are proposing a novel method that makes it possible to analyze high dimensional data with arbitrary shaped projected clusters and high noise levels. At the core of our method lies the idea of subspace validity. We map the data in a way that allows us to test the quality of subspaces using statistical tests. Experimental results, both on synthetic and real data sets, demonstrate the potential of our method. © 2003 IEEE.
Original languageEnglish
JournalProceedings - IEEE International Conference on Data Mining, ICDM
StatePublished - 1 Dec 2003


Dive into the research topics of 'Analyzing high-dimensional data by subspace validity'. Together they form a unique fingerprint.

Cite this