We explore the framework of permutation-based p-values for assessing the performance of
classifiers. In this paper we study two simple permutation tests. The first test assess whether …

Recent research has highlighted the practical benefits of subjective interestingness
measures, which quantify the novelty or unexpectedness of a pattern when contrasted with …

We formalize the data mining process as a process of information exchange, defined by the
following key components. The data miner's state of mind is modeled as a probability …

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 …

Statistical evaluation of biclustering solutions is essential to guarantee the absence of
spurious relations and to validate the high number of scientific statements inferred from …

In this position paper, we discuss how different branches of research on clustering and
pattern mining, while rather different at first glance, in fact have a lot in common and can …

There is a wide variety of data mining methods available, and it is generally useful in
exploratory data analysis to use many different methods for the same dataset. This, however …

We introduce a novel class of null models for the statistical validation of results obtained
from binary transactional and sequence datasets. Our null models are Row-Order Agnostic …

This paper suggests a framework for mining subjectively interesting pattern sets that is
based on two components:(1) the encoding of prior information in a model for the data …

Agile software development is a kind of lightweight development method, which can satisfy
to the changes of requirements. This paper applies formal methods into agile software …