High-dimensional data refers to datasets that have a large number of features or variables, often exceeding the number of observations. This type of data is common in fields like bioinformatics, where each sample may be characterized by thousands of genes, making it challenging to analyze. The curse of dimensionality comes into play here, as the complexity increases with more dimensions, affecting algorithms and clustering methods used for analysis.
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