sparse k-means
[/spɑrs keɪ-minz/]
noun
k-médias esparsa
1. A machine learning clustering algorithm that extends the standard k-means method by incorporating sparsity constraints to identify a subset of relevant features for clustering high-dimensional data
Sparse k-means is particularly useful for clustering gene expression data where only a small number of genes are informative.
K-médias esparsa é particularmente útil para agrupar dados de expressão gênica onde apenas um pequeno número de genes é informativo.
2. A technique that performs simultaneous feature selection and clustering by enforcing L1 penalties on feature weights during the clustering process
The sparse k-means algorithm reduced the dimensionality while maintaining clustering accuracy.
O algoritmo de k-médias esparsa reduziu a dimensionalidade mantendo a precisão do agrupamento.
Sparse k-means is primarily used in academic and research contexts, particularly in bioinformatics, computer science, and statistics. The term is technical and domain-specific, rarely appearing in casual conversation. In Brazil, the English term is often retained in technical documentation, though Portuguese translations (k-médias esparsa) are increasingly used in academic publications and university courses.
Related Idioms & Phrases
feature selection in clustering
dimensionality curse in machine learning
penalized optimization methods
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