Similarity Search in Medical Data

The ongoing automation in our modern information society leads to a tremendous rise in the amount as well as complexity of collected data. In medical imaging for example the electronic availability of extensive data collected as part of clinical trials provides a remarkable potentiality to detect new relevant features in complex diseases like brain tumors. Using data mining applications for the analysis of the data raises several problems. One problem is the localization of outstanding observations also called outliers in a data set. In this work a technique for parameter-free outlier detection, which is based on data compression and a general data model which combines the Generalized Normal Distribution (GND) with independent components, to cope with existing problems like parameter settings or implicit data distribution assumptions, is proposed. Another problem in many modern applications amongst others in medical imaging is the efficient similarity search in uncertain data. At present, an adequate therapy planning of newly detected brain tumors assumedly of glial origin needs invasive biopsy due to the fact that prognosis and treatment, both vary strongly for benign, low-grade, and high-grade tumors. To date differentiation of tumor grades is mainly based on the expertise of neuroradiologists examining contrast-enhanced Magnetic Resonance Images (MRI). To assist neuroradiologist experts during the differentiation between tumors of different malignancy we proposed a novel, efficient similarity search technique for uncertain data. The feature vector of an object is thereby not exactly known but is rather defined by a Probability Density Function (PDF) like a Gaussian Mixture Model (GMM). Previous work is limited to axis-parallel Gaussian distributions, hence, correlations between different features are not considered in these similarity searches. In this work a novel, efficient similarity search technique for general GMMs without independence assumption is presented. The actual components of a GMM are approximated in a conservative but tight way. The conservativity of the approach leads to a filter-refinement architecture, which guarantees no false dismissals and the tightness of the approximations causes good filter selectivity. An extensive experimental evaluation of the approach demonstrates a considerable speed-up of similarity queries on general GMMs. Additionally, promising results for advancing the differentiation between brain tumors of different grades could be obtained by applying the approach to four-dimensional Magnetic Resonance Images of glioma patients

Coping with new Challenges in Clustering and Biomedical Imaging

The last years have seen a tremendous increase of data acquisition in different scientific fields such as molecular biology, bioinformatics or biomedicine. Therefore, novel methods are needed for automatic data processing and analysis of this large amount of data. Data mining is the process of applying methods like clustering or classification to large databases in order to uncover hidden patterns. Clustering is the task of partitioning points of a data set into distinct groups in order to minimize the intra cluster similarity and to maximize the inter cluster similarity. In contrast to unsupervised learning like clustering, the classification problem is known as supervised learning that aims at the prediction of group membership of data objects on the basis of rules learned from a training set where the group membership is known. Specialized methods have been proposed for hierarchical and partitioning clustering. However, these methods suffer from several drawbacks. In the first part of this work, new clustering methods are proposed that cope with problems from conventional clustering algorithms. ITCH (Information-Theoretic Cluster Hierarchies) is a hierarchical clustering method that is based on a hierarchical variant of the Minimum Description Length (MDL) principle which finds hierarchies of clusters without requiring input parameters. As ITCH may converge only to a local optimum we propose GACH (Genetic Algorithm for Finding Cluster Hierarchies) that combines the benefits from genetic algorithms with information-theory. In this way the search space is explored more effectively. Furthermore, we propose INTEGRATE a novel clustering method for data with mixed numerical and categorical attributes. Supported by the MDL principle our method integrates the information provided by heterogeneous numerical and categorical attributes and thus naturally balances the influence of both sources of information. A competitive evaluation illustrates that INTEGRATE is more effective than existing clustering methods for mixed type data. Besides clustering methods for single data objects we provide a solution for clustering different data sets that are represented by their skylines. The skyline operator is a well-established database primitive for finding database objects which minimize two or more attributes with an unknown weighting between these attributes. In this thesis, we define a similarity measure, called SkyDist, for comparing skylines of different data sets that can directly be integrated into different data mining tasks such as clustering or classification. The experiments show that SkyDist in combination with different clustering algorithms can give useful insights into many applications. In the second part, we focus on the analysis of high resolution magnetic resonance images (MRI) that are clinically relevant and may allow for an early detection and diagnosis of several diseases. In particular, we propose a framework for the classification of Alzheimer's disease in MR images combining the data mining steps of feature selection, clustering and classification. As a result, a set of highly selective features discriminating patients with Alzheimer and healthy people has been identified. However, the analysis of the high dimensional MR images is extremely time-consuming. Therefore we developed JGrid, a scalable distributed computing solution designed to allow for a large scale analysis of MRI and thus an optimized prediction of diagnosis. In another study we apply efficient algorithms for motif discovery to task-fMRI scans in order to identify patterns in the brain that are characteristic for patients with somatoform pain disorder. We find groups of brain compartments that occur frequently within the brain networks and discriminate well among healthy and diseased people

Efficient Knowledge Extraction from Structured Data

Knowledge extraction from structured data aims for identifying valid, novel, potentially useful, and ultimately understandable patterns in the data. The core step of this process is the application of a data mining algorithm in order to produce an enumeration of particular patterns and relationships in large databases. Clustering is one of the major data mining tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the similarity of objects from different clusters is minimized. In this thesis, we advance the state-of-the-art data mining algorithms for analyzing structured data types. We describe the development of innovative solutions for hierarchical data mining. The EM-based hierarchical clustering method ITCH (Information-Theoretic Cluster Hierarchies) is designed to propose solid solutions for four different challenges. (1) to guide the hierarchical clustering algorithm to identify only meaningful and valid clusters. (2) to represent each cluster content in the hierarchy by an intuitive description with e.g. a probability density function. (3) to consistently handle outliers. (4) to avoid difficult parameter settings. ITCH is built on a hierarchical variant of the information-theoretic principle of Minimum Description Length (MDL). Interpreting the hierarchical cluster structure as a statistical model of the dataset, it can be used for effective data compression by Huffman coding. Thus, the achievable compression rate induces a natural objective function for clustering, which automatically satisfies all four above mentioned goals. The genetic-based hierarchical clustering algorithm GACH (Genetic Algorithm for finding Cluster Hierarchies) overcomes the problem of getting stuck in a local optimum by a beneficial combination of genetic algorithms, information theory and model-based clustering. Besides hierarchical data mining, we also made contributions to more complex data structures, namely objects that consist of mixed type attributes and skyline objects. The algorithm INTEGRATE performs integrative mining of heterogeneous data, which is one of the major challenges in the next decade, by a unified view on numerical and categorical information in clustering. Once more, supported by the MDL principle, INTEGRATE guarantees the usability on real world data. For skyline objects we developed SkyDist, a similarity measure for comparing different skyline objects, which is therefore a first step towards performing data mining on this kind of data structure. Applied in a recommender system, for example SkyDist can be used for pointing the user to alternative car types, exhibiting a similar price/mileage behavior like in his original query. For mining graph-structured data, we developed different approaches that have the ability to detect patterns in static as well as in dynamic networks. We confirmed the practical feasibility of our novel approaches on large real-world case studies ranging from medical brain data to biological yeast networks. In the second part of this thesis, we focused on boosting the knowledge extraction process. We achieved this objective by an intelligent adoption of Graphics Processing Units (GPUs). The GPUs have evolved from simple devices for the display signal preparation into powerful coprocessors that do not only support typical computer graphics tasks but can also be used for general numeric and symbolic computations. As major advantage, GPUs provide extreme parallelism combined with a high bandwidth in memory transfer at low cost. In this thesis, we propose algorithms for computationally expensive data mining tasks like similarity search and different clustering paradigms which are designed for the highly parallel environment of a GPU, called CUDA-DClust and CUDA-k-means. We define a multi-dimensional index structure which is particularly suited to support similarity queries under the restricted programming model of a GPU. We demonstrate the superiority of our algorithms running on GPU over their conventional counterparts on CPU in terms of efficiency

Finding correlations and independences in omics data

Biological studies across all omics fields generate vast amounts of data. To understand these complex data, biologically motivated data mining techniques are indispensable. Evaluation of the high-throughput measurements usually relies on the identification of underlying signals as well as shared or outstanding characteristics. Therein, methods have been developed to recover source signals of present datasets, reveal objects which are more similar to each other than to other objects as well as to detect observations which are in contrast to the background dataset. Biological problems got individually addressed by using solutions from computer science according to their needs. The study of protein-protein interactions (interactome) focuses on the identification of clusters, the sub-graphs of graphs: A parameter-free graph clustering algorithm was developed, which was based on the concept of graph compression, in order to find sets of highly interlinked proteins sharing similar characteristics. The study of lipids (lipidome) calls for co-regulation analyses: To reveal those lipids similarly responding to biological factors, partial correlations were generated with differential Gaussian Graphical Models while accounting for solely disease-specific correlations. The study on single cell level (cytomics) aims to understand cellular systems often with the help of microscopy techniques: A novel noise robust source separation technique allowed to reliably extract independent components from microscopy images describing protein behaviors. The study of peptides (peptidomics) often requires the detection outstanding observations: By assessing regularities in the data set, an outlier detection algorithm was implemented based on compression efficacy of independent components of the dataset. All developed algorithms had to fulfill most diverse constraints in each omics field, but were met with methods derived from standard correlation and dependency analyses

Synchronization Inspired Data Mining

Advances of modern technologies produce huge amounts of data in various fields, increasing the need for efficient and effective data mining tools to uncover the information contained implicitly in the data. This thesis mainly aims to propose innovative and solid algorithms for data mining from a novel perspective: synchronization. Synchronization is a prevalent phenomenon in nature that a group of events spontaneously come into co-occurrence with a common rhythm through mutual interactions. The mechanism of synchronization allows controlling of complex processes by simple operations based on interactions between objects. The first main part of this thesis focuses on developing the innovative algorithms for data mining. Inspired by the concept of synchronization, this thesis presents Sync (Clustering by Synchronization), a novel approach to clustering. In combination with the Minimum Description Length principle (MDL), it allows discovering the intrinsic clusters without any data distribution assumptions and parameters setting. In addition, relying on the dierent dynamic behaviors of objects during the process towards synchronization,the algorithm SOD (Synchronization-based Outlier Detection) is further proposed. The outlier objects can be naturally flagged by the denition of Local Synchronization Factor (LSF). To cure the curse of dimensionality in clustering,a subspace clustering algorithm ORSC is introduced which automatically detects clusters in subspaces of the original feature space. This approach proposes a weighted local interaction model to ensure all objects in a common cluster, which accommodate in arbitrarily oriented subspace, naturally move together. In order to reveal the underlying patterns in graphs, a graph partitioning approach RSGC (Robust Synchronization-based Graph Clustering) is presented. The key philosophy of RSGC is to consider graph clustering as a dynamic process towards synchronization. Inherited from the powerful concept of synchronization, RSGC shows several desirable properties that don't exist in other competitive methods. For all presented algorithms, their efficiency and eectiveness are thoroughly analyzed. The benets over traditional approaches are further demonstrated by evaluating them on synthetic as well as real-world data sets. Not only the theory research on novel data mining algorithms, the second main part of the thesis focuses on brain network analysis based on Diusion Tensor Images (DTI). A new framework for automated white matter tracts clustering is rst proposed to identify the meaningful ber bundles in the Human Brain by combining ideas from time series mining with density-based clustering. Subsequently, the enhancement and variation of this approach is discussed allowing for a more robust, efficient, or eective way to find hierarchies of ber bundles. Based on the structural connectivity network, an automated prediction framework is proposed to analyze and understand the abnormal patterns in patients of Alzheimer's Disease