The problem of simultaneously clustering columns and rows (coclustering) arises in important applications, such as text data mining, microarray analysis, and recommendation system...
Linear Discriminant Analysis (LDA) is one of the most popular approaches for feature extraction and dimension reduction to overcome the curse of the dimensionality of the high-dime...
Despite advances in the application of automated statistical and machine learning techniques to system log and trace data there will always be a need for human analysis of machine...
Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining, machine learning, and bioinformatics. Friedman proposed a compromise between Linear ...
Background: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) ...