Detecting Unusual Behaviour and Mining Unstructured Data

Alexander Balinsky (), Helen Balinsky () and Steven Simske ()
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Alexander Balinsky: Cardiff University, Cardiff School of Mathematics
Helen Balinsky: Hewlett-Packard Laboratories
Steven Simske: Hewlett-Packard Laboratories

A chapter in UK Success Stories in Industrial Mathematics, 2016, pp 181-187 from Springer

Abstract: 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.

Keywords: Mining Unstructured Data; Helmholtz Principle; Cardiff University School; Gestalt Theory; Extractive Text Summarization (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-25454-8_23

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DOI: 10.1007/978-3-319-25454-8_23

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