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Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease
TLDR
We developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. Expand
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Density-based clustering using graphics processors
TLDR
In this paper, we propose CUDA-DClust, a massively parallel algorithm for density-based clustering for the use of a Graphics Processing Unit (GPU). Expand
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Subspace selection for clustering high-dimensional data
TLDR
We present a feature selection technique called SURFING (subspaces relevant for clustering) that finds all relevant subspaces in high dimensional, sparse data sets and sorts them by relevance and produces better results than comparative methods. Expand
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Efficiently Processing Continuous k-NN Queries on Data Streams
TLDR
Efficiently processing continuous k-nearest neighbor queries on data streams is important in many application domains. Expand
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Decoding an individual's sensitivity to pain from the multivariate analysis of EEG data.
The perception of pain is characterized by its tremendous intra- and interindividual variability. Different individuals perceive the very same painful event largely differently. Here, we aimed toExpand
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Clustering by synchronization
TLDR
Inspired by the powerful concept of synchronization, we propose Sync, a novel approach to clustering based on synchronization. Expand
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Robust Automated Detection of Microstructural White Matter Degeneration in Alzheimer’s Disease Using Machine Learning Classification of Multicenter DTI Data
Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on itsExpand
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INCONCO: interpretable clustering of numerical and categorical objects
TLDR
In this paper, we approach both challenges by constructing a relationship to the concept of data compression using the Minimum Description Length principle: a detected cluster structure is the better the more efficient it can be exploited for data compression. Expand
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Robust information-theoretic clustering
TLDR
We propose a robust framework for determining a natural clustering of a given data set, based on the minimum description length (MDL) principle. Expand
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Dependency clustering across measurement scales
  • C. Plant
  • Mathematics, Computer Science
  • KDD
  • 12 August 2012
TLDR
We present the algorithm Scenic for dependency clustering across measurement scales. Expand
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