Constraint-based discriminative dimension selection for high-dimensional stream clustering

Integrated constraint based clustering algorithm for high dimensional data

Dimension selection, dimension weighting and data assignment are three circular dependent essential tasks for high dimensional data clustering and each such task is challenging. To meet the challenge of high dimensional data clustering, constraints have been employed in several previous works. However, these constraint based algorithms use constraints to help accomplish only one of the three es...

Constraint Based Subspace Clustering for High Dimensional Uncertain Data

Pairwise variable selection for high-dimensional model-based clustering.

Variable selection for clustering is an important and challenging problem in high-dimensional data analysis. Existing variable selection methods for model-based clustering select informative variables in a "one-in-all-out" manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applicat...

Attribute Selection for High Dimensional Data Clustering

We present a new method to select an attribute subset (with few or no loss of information) for high dimensional data clustering. Most of existing clustering algorithms loose some of their efficiency in high dimensional data sets. One possible solution is to use only a subset of the whole set of dimensions. But the number of possible dimension subsets is too large to be fully parsed. We use a he...

Feature Selection For High-Dimensional Clustering

We present a nonparametric method for selecting informative features in high-dimensional clustering problems. We start with a screening step that uses a test for multimodality. Then we apply kernel density estimation and mode clustering to the selected features. The output of the method consists of a list of relevant features, and cluster assignments. We provide explicit bounds on the error rat...