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We describe a general approach for detecting data alignments in large unordered noisy multidimensional datasets.
The main challenge is high memory demand due to oversampling, especially when multidimensional datasets are involved.
Data summarization can reduce the size and complexity of multidimensional datasets.
As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets.
The core issue here is to effectively representing multidimensional datasets without overwhelming the human ability to comprehend the resulting graphs.
This paper proposes a method based on complex networks analysis, devised to perform clustering on multidimensional datasets.
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The alternative is to exploit preclinical imaging to produce a multidimensional dataset in live animals.
A skyline query identifies a set of non-dominated data records in a multidimensional dataset.
LOF is a probability based method for outlier detection in a multidimensional dataset [51].
The SOM models represent a multidimensional dataset by means of a bidimensional matrix of features, which can be explored for analysis and estimation purposes.
Panel data or longitudinal data refers to a multidimensional dataset that observes series of cross-sectional data over time (Baltagi 2013).
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