Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dimensionality reduction, Linear Discriminant Analysis (LDA) is one of the most po...
Feiping Nie, Shiming Xiang, Yangqiu Song, Changshu...
—It has been a long way since the beginnings of metric space searching, where people coming from algorithmics tried to apply their background to this new paradigm, obtaining vari...
Abstract--Randomization is a general technique for evaluating the significance of data analysis results. In randomizationbased significance testing, a result is considered to be in...
We are designing new data mining techniques on boolean contexts to identify a priori interesting bi-sets (i.e., sets of objects or transactions associated to sets of attributes or ...
— While a number of information visualization software frameworks exist, creating new visualizations, especially those that involve novel visualization metaphors, interaction tec...