In supervised learning, an important issue usually not taken into account by classical methods is that a class represented in the test set may have not been encountered earlier in the learning phase. Classical supervised algorithms will automatically label such observations as belonging to one...

A column generation based approach is proposed for solving the cluster-wise regression problem. The proposed strategy relies firstly on several efficient heuristic strategies to insert columns into the restricted master problem. If these heuristics fail to identify an improving column, an...

As data sets continue to grow in size and complexity, effective and efficient techniques are needed to target important features in the variable space. Many of the variable selection techniques that are commonly used alongside clustering algorithms are based upon determining the best variable...

Multiple imputation is one of the most highly recommended procedures for dealing with missing data. However, to date little attention has been paid to methods for combining the results from principal component analyses applied to a multiply imputed data set. In this paper we propose Generalized...

We describe a simple time series transformation to detect differences in series that can be accurately modelled as stationary autoregressive (AR) processes. The transformation involves forming the histogram of above and below the mean run lengths. The run length (RL) transformation has the...

It is shown that if cell weights may be calculated from the data the chance-corrected Zegers-ten Berge coefficients for metric scales are special cases of Cohen’s weighted kappa. The corrected coefficients include Pearson’s product-moment correlation, Spearman’s rank correlation and the...