Current Projects (Group Böhm)

Efficient search in uncertain data by the use of index structures for probability density functions

The well-established procedure for modelling uncertain data is to assign probability density functions to the objects. For this purpose, most of the existing approaches only use a relatively simple model, namely axis parallel Gaussians. In this project, we define a very general notion for modelling the uncertainty, based on Exponential Power Distributions and Gaussian Mixture Models. In addition, we want to develop suitable index structures. For this project we identified a wide application field ranging from image search over medicine to biometrics.

Multimodal Data Mining to analyze brain functions in state of rest

The spontaneous brain activity in the state of rest has been explored insufficiently. The image generating methods, e.g. EEG and fMRI, provide information on the intrinsic brain activity i.e. they demonstrate the interaction of the brain areas within various networks that are characterized by exalted reciprocal functional connectivity. First findings demonstrate that psychiatrical and neurological pathologies like Alzheimer's disease and depressions modify the complex interactions of these networks. The current project is aimed at the development of a data-mining technique that should analyse the static data obtained through various imaging procedures. The project's framework applies and develops the clustering and data-mining methods.

Innovative Data Mining Verfahren für die Analyse genetischer, phänotypischer und epidemiologischer Daten zur Erforschung von psychiatrischen Erkrankungen

Clustering algorithms for the analysis of Diffusion Tensor Images (DTI)

Understanding the human brain connectivity is an elusive goal for neuroscience due to its complex and the limitation of technologies in past several centuries, especially for the anatomical connectivity. Currently, the emergence of related DTI technologies offer a promising way to explore the anatomical pattern in vivo. This project aims to understand the brain connectivity using related data mining techniques. The key objectives are as follows:

• Automatically clustering the 3D white matter tracts;
• Quantitative analysis of fiber bundles with different groups;
• Investigating the relationship between structural connectivity and functional connectivity.

Former Projects (Group Böhm)

Detection of spatial patterns of atrophy in Alzheimers Disease from structural MRI

Magnetic resonance imaging (MRI) allows to display brain structures with highest resolution. To fully exploit the potential of this imagining modality, data mining methods are required to identify subtle differences in brain structure caused by disorders such as Mild Cognitive Impairment and early stage Alzheimers disease. We proposed a data mining framework which combines elements from feature selection, clustering, classification and provides a concise visualization of affected areas in the brain.

Temporal Graph-Mining based on the Rough Graph Theory

In this project we combined the Rough Graph Theory and the idea of Markov Chains to model dynamic systems, like metabolic pathways, intra-cellular signal cascades or activation-profiles of the human brain. Within the scope of this project, we aimed to support the semi-automatic detection of such systems by an interactive data mining process.

Efficient and effective data mining solutions for high dimensional data

Increasing automation and networking of the modern information society leads to an increase volume of data as well as complexity of the data. Therefore in the semi-automatic analysis using data mining methods like clustering following problems emerge: Many features are measured or collected automatically but are irrelevant for particular data mining approaches as these can be noisy or redundant. As in real data typically different sets of features are relevant for different groups of objects methods for reducing the feature space do not necessary lead to satisfactory results. For this reason development and analysis of methods for subspace or correlation clustering is needed to individually and flexiblly scale high-dimensional feature spaces to low dimensions. The aim of the project was to develp innovative data mining methods for high-dimensional feature spaces.

Data Mining in biomedical data

In the context of this project, we developed a classification model for newborns that suffer from Phenylketonuria (PKU), an autosomal recessive genetic disorder. For this purpose, we only used parameters of metabolic pathways (amino acids) that are determined by mass spectrometry. Further optimizations were achieved by feature selection and reduction of the dimensionality (e.g. by PCA) of the feature space. In contrast to a "golden standard" data set, we obtained values for sensitivity and selectivity of more than 99%.