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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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Ravindra Babu, T. (Author), Narasimha Murty, M. (Author), Subrahmanya, S.V (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:Inglés
Published: London : Springer London : Imprint: Springer, 2013.
Edition:1st ed. 2013.
Series:Advances in Computer Vision and Pattern Recognition,
Subjects:
Pattern recognition systems.
Data mining.
Artificial intelligence.
Automated Pattern Recognition.
Data Mining and Knowledge Discovery.
Artificial Intelligence.
Online Access:Texto Completo
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.

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