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Detecting Unusual Behaviour and Mining Unstructured Data

In: UK Success Stories in Industrial Mathematics

Author

Listed:
  • Alexander Balinsky

    (Cardiff University, Cardiff School of Mathematics)

  • Helen Balinsky

    (Hewlett-Packard Laboratories)

  • Steven Simske

    (Hewlett-Packard Laboratories)

Abstract

Keyword and feature extraction is a fundamental problem in data mining and document processing. A majority of applications directly depend on the quality and speed of keyword and feature extraction pre-processing results. In the current paper we present novel algorithms for feature extraction and change detection in unstructured data, primarily in textual and sequential data. Our approach is based on ideas from image processing and especially on the Helmholtz Principle from the Gestalt Theory of human perception. The improvements due to the novel feature extraction technique are demonstrated on several key applications: classification for strengthening document security and storage optimization, automatic summarization and segmentation for problems of information overload. The developed algorithms and applications are the result of research collaboration between Cardiff University School of Mathematics and HP Laboratories.

Suggested Citation

  • Alexander Balinsky & Helen Balinsky & Steven Simske, 2016. "Detecting Unusual Behaviour and Mining Unstructured Data," Springer Books, in: Philip J. Aston & Anthony J. Mulholland & Katherine M.M. Tant (ed.), UK Success Stories in Industrial Mathematics, edition 1, pages 181-187, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-25454-8_23
    DOI: 10.1007/978-3-319-25454-8_23
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