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...
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| Автори: | , , |
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| Співавтор: | |
| Формат: | eКнига |
| Мова: | English |
| Опубліковано: |
London
Springer London Imprint: Springer,
2013.
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| Серія: | Advances in Computer Vision and Pattern Recognition,
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| Предмети: | |
| Онлайн доступ: | Click here to view the full text content |
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Зміст:
- 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.