Nov 18, 2009 · Traditional similarity measurements often become meaningless when dimensions of datasets increase. Subspace clustering has been proposed to ...

Subspace clustering has been proposed to find clus- ters embedded in subspaces of high dimensional datasets. Many existing algorithms use a grid based approach ...

Nov 18, 2009 · Many subspace clustering algorithms use a grid-based approach to find dense regions[6]-[9]. They partition the data space into nonoverlapping ...

The experiment results show that the nCluster model can indeed preserve clusters that are shattered by the grid‐based approach on synthetic datasets, ...

Traditional similarity measurements often become meaningless when dimensions of datasets increase. Subspace clustering has been proposed to find clusters ...

Top-down algorithms find an initial clustering in the full set of dimensions and evaluate the subspaces of each cluster, iteratively improving the results.

dc.date.issued, 2009 ; dc.identifier.citation, Liu, G.,Sim, K.,Li, J.,Wong, L. (2009). Efficient mining of distance-based subspace clusters. Statistical Analysis ...

Subspace clustering is an extension of traditional cluster- ing that seeks to find clusters in different subspaces within a dataset.

In this work a novel algorithm, named DOLPHIN, for detecting distance-based outliers is presented. The proposed algorithm performs only two sequential scans ...

This method integrates with background knowledge and importantly instance level constraints to speed up the subspace clustering. It applied in both density and ...