A framework for generalized subspace pattern mining in high-dimensional datasets

A Mutual Subspace Clustering Algorithm for High Dimensional Datasets

Generation of consistent clusters is always an interesting research issue in the field of knowledge and data engineering. In real applications, different similarity measures and different clustering techniques may be adopted in different clustering spaces. In such a case, it is very difficult or even impossible to define an appropriate similarity measure and clustering criteria in the union spa...

Visualization & Data Mining for High Dimensional Datasets

Interactive Subspace Clustering for Mining High-Dimensional Spatial Patterns

The unprecedented large size and high dimensionality of existing geographic datasets make complex patterns that potentially lurk in the data hard to find. Spatial data analysis capabilities currently available have not kept up with the need for deriving the full potential of these data. “Traditional spatial analytical techniques cannot easily discover new and unexpected patterns, trends and rel...

A New Inexact Inverse Subspace Iteration for Generalized Eigenvalue Problems

In this paper, we represent an inexact inverse subspace iteration method for computing a few eigenpairs of the generalized eigenvalue problem Ax = Bx [Q. Ye and P. Zhang, Inexact inverse subspace iteration for generalized eigenvalue problems, Linear Algebra and its Application, 434 (2011) 1697-1715 ]. In particular, the linear convergence property of the inverse subspace iteration is preserved.

DBSC: A Dependency-Based Subspace Clustering Algorithm for High Dimensional Numerical Datasets

We present a novel algorithm called DBSC, which finds subspace clusters in numerical datasets based on the concept of “dependency”. This algorithm uses a depth-first search strategy to find out the maximal subspaces: a new dimension is added to current k-subspace and its validity as a (k 1)-subspace is evaluated. The clusters within those maximal subspaces are mined in a similar fashion as maxi...