Large data is challenging for most existing discovery algorithms, for several reasons. First of
all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible …

It is known that productive pattern discovery from data has to interactively involve the user as
directly as possible. State-of-the-art toolboxes require the specification of sophisticated …

Existing algorithms for subgroup discovery with numerical targets do not optimize the error
or target variable dispersion of the groups they find. This often leads to unreliable or …

Data mining is used to extract important information from the bulk of data to save it and
summarize it in effective manner. The hidden information can be extracted from the large set …

Exceptional preferences mining (EPM) is a crossover between two subfields of data mining:
local pattern mining and preference learning. EPM can be seen as a local pattern mining …

The objective of subgroup discovery is to find groups of individuals who are statistically
different from others in a large data set. Most existing measures of the quality of subgroups …

Structural equation modeling is one of the most popular statistical frameworks in the social
and behavioral sciences. Often, detection of groups with distinct sets of parameters in …

Understanding the socio-economical background of voters supporting a certain cause or,
vice versa, understanding the political stance of people from a certain socio-economical …

Novel Techniques for Efficient and Effective Subgroup Discovery Page 1 Novel Techniques
for Efficient and Effective Subgroup Discovery vorgelegt von Florian Lemmerich Dissertation …

For the task of subgroup discovery, generalization-aware interesting measures that are
based not only on the statistics of the patterns itself, but also on the statistics of their …