• Corpus ID: 63210118

Compression Schemes for Mining Large Datasets: A Machine Learning Perspective

@inproceedings{Babu2013CompressionSF,
  title={Compression Schemes for Mining Large Datasets: A Machine Learning Perspective},
  author={T. Ravindra Babu and M. Narasimha Murty and S. V. Subrahmanya},
  year={2013}
}
This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features:describes a non-lossy compression scheme based on run-length encoding of… 
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In Situ Summarization with VTK-m

This work presents a framework created by integrating existing, production-ready projects and provides timings of two particular algorithms that serve as exemplars for summarization: a wavelet-based data reduction filter and a generator for creating image-like databases of extracted features.

Compression Schemes for Mining Large Datasets