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...
Main Authors: | , , |
---|---|
Format: | eBook |
Language: | English |
Published: |
London
Springer London
2013, 2013
|
Edition: | 1st ed. 2013 |
Series: | Advances in Computer Vision and Pattern Recognition
|
Subjects: | |
Online Access: | |
Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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