Enhanced subspace clustering - Sim, Kelvin Sian Hui. (2012)
Subspace clustering overcomes the curse of dimensionality that traditional clustering suffered, by finding groups of objects that are homogeneous in subspaces of the data, instead of the full space. Research on basic subspace clustering over the past decade primary focuses on finding groups of objects that are closed together in subspaces of 2D data. Recently, the proliferation of non-traditional data and the need for higher quality clustering results have shifted the research paradigm to enhanced subspace clustering, which focuses on problems that cannot be handled or solved effectively through basic subspace clustering.