We provide evidence that non-linear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define ...
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution...
Many emerging application domains require database systems to support efficient access over highly multidimensional datasets. The current state-of-the-art technique to indexing hi...
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...
Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through...