Comparing segmentations by applying randomization techniques

Randomization Techniques for Graphs

Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those results is lacking. Within the framework of statistical hypothesis testing, we focus in this paper on randomization techniques for unweighted undirected graphs. Randomization is an important approach to assess the statisti...

Comparing cost prediction models by resampling techniques

The accurate software cost prediction is a research topic that has attracted much of the interest of the software engineering community during the latest decades. A large part of the research efforts involves the development of statistical models based on historical data. Since there are a lot of models that can be fitted to certain data, a crucial issue is the selection of the most efficient p...

Approximations and Randomization to Boost CSP Techniques

In recent years we have seen an increasing interest in combining constraint satisfaction problem (CSP) formulations and linear programming (LP) based techniques for solving hard computational problems. While considerable progress has been made in the integration of these techniques for solving problems that exhibit a mixture of linear and combinatorial constraints, it has been surprisingly diff...

Using randomization techniques to analyse behavioural data

Data from behavioural studies are frequently non-normally distributed and cannot be analysed with traditional parametric statistics. Instead, behaviourists must rely on rank-transformation tests, which lose potentially valuable information present in the data. Recently, however, biologists in other disciplines have resolved similar statistical difficulties by using resampling methods. Results f...

Applying microarray ‐ based techniques