 
Summary: UNIVERSITY OF REGINA
Department of Mathematics and Statistics
Graduate Student Seminar
Speaker: Yuxin (Sheena) Zhang
Date: 30 August 2005
Time: 10.00 o'clock
Location: College West 307.18 (Math & Stats Lounge)
Title: Subspace Clustering of High Dimensional Data
Abstract: I am going to talk about subspace clustering high dimensional data, based on
the minimization of an objective function for clustering.
Clustering is one of the frequently used tools in Spatial Data Mining. Finding clusters
in highdimensional data is usually futile. But highdimensional data may be clustered
differently in varying subspaces of the feature space. Subspace clustering aims at finding
all subspaces of highdimensional data in which clusters exist. So the goal is to discover
some relationship among all the variables.
Clustering suffers from the curse of dimensionality. In many real world problems, some
points are correlated with respect to a given set of dimensions, and others are correlated
with respect to different dimensions. Each dimension could be relevant to at least one
of the clusters. In high dimensional spaces, for any given pair of points within the same
cluster, there exist at least a few dimensions on which the points are far apart from each
