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Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform
When facing high-dimensional data streams, clustering algorithms quickly reach the boundaries of their usefulness as most of these methods are not designed to deal with the curse of dimensionality. Due to inherent sparsity in high-dimensional data, distances between objects tend to become meaningles...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206268/ http://dx.doi.org/10.1007/978-3-030-47426-3_28 |