Compression schemes for mining large datasets : a machine learning perspective /
As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and inefficient computation times. This unique text/reference addresses the challenges of data abstraction generation using a least number of database scans, co...
Guardat en:
| Autors principals: | , , |
|---|---|
| Autor corporatiu: | |
| Format: | eBook |
| Idioma: | English |
| Publicat: |
London
Springer London Imprint: Springer,
2013.
|
| Col·lecció: | Advances in Computer Vision and Pattern Recognition,
|
| Matèries: | |
| Accés en línia: | Click here to view the full text content |
| Etiquetes: |
Afegir etiqueta
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
Taula de continguts:
- Introduction
- Data Mining Paradigms
- Run-Length Encoded Compression Scheme
- Dimensionality Reduction by Subsequence Pruning
- Data Compaction through Simultaneous Selection of Prototypes and Features
- Domain Knowledge-Based Compaction
- Optimal Dimensionality Reduction
- Big Data Abstraction through Multiagent Systems
- Intrusion Detection Dataset: Binary Representation.