Large numbers of dimensions not only cause clutter in multidimensional visualizations, but also make it difficult for users to navigate the data space. Effective dimension manage...
Jing Yang, Wei Peng, Matthew O. Ward, Elke A. Rund...
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and unce...
Abstract. Nearest-neighbor queries in high-dimensional space are of high importance in various applications, especially in content-based indexing of multimedia data. For an optimiz...
Parametric Embedding (PE) has recently been proposed as a general-purpose algorithm for class visualisation. It takes class posteriors produced by a mixture-based clustering algori...
We propose an efficient sampling based outlier detection method for large high-dimensional data. Our method consists of two phases. In the first phase, we combine a "sampling...
Timothy de Vries, Sanjay Chawla, Pei Sun, Gia Vinh...