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...
Spremljeno u:
| Glavni autori: | , , |
|---|---|
| Autor kompanije: | |
| Format: | e-knjiga |
| Jezik: | English |
| Izdano: |
London
Springer London Imprint: Springer,
2013.
|
| Serija: | Advances in Computer Vision and Pattern Recognition,
|
| Teme: | |
| Online pristup: | Click here to view the full text content |
| Oznake: |
Dodaj oznaku
Bez oznaka, Budi prvi tko označuje ovaj zapis!
|
Sadržaj:
- 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.