The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
: Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerate...
We investigate how random projection can best be used for clustering high dimensional data. Random projection has been shown to have promising theoretical properties. In practice,...
Clustering large data sets with high dimensionality is a challenging data-mining task. This paper presents a framework to perform such a task efficiently. It is based on the notio...
Ying Lai, Ratko Orlandic, Wai Gen Yee, Sachin Kulk...
Understanding high-dimensional real world data usually requires learning the structure of the data space. The structure maycontain high-dimensional clusters that are related in co...