On the Helmholtz Principle for data mining

Balinsky, Alexander ORCID: https://orcid.org/0000-0002-8151-4462, Balinsky, Helen and Simske, Steven 2017. On the Helmholtz Principle for data mining. Kreinovich, Vladek, ed. Uncertainty Modeling, Vol. 683. Studies in Computational Intelligence, Springer, pp. 15-35.

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Abstract

Keyword and feature extraction is a fundamental problem in text data mining and document processing. A majority of document processing applications directly depend on the quality and speed of keyword extraction algorithms. In this article, an approach, introduced in [1], to rapid change detection in data streams and documents is developed and analysed. It is based on ideas from image processing and especially on the Helmholtz Principle from the Gestalt Theory of human perception. Applied to the problem of keywords extraction, it delivers fast and effective tools to identify meaningful keywords using parameter-free methods. We also define a level of meaningfulness of the keywords which can be used to modify the set of keywords depending on application needs.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
ISBN:	9783319510521
Related URLs:	Publisher
Last Modified:	02 Nov 2022 10:21
URI:	https://orca.cardiff.ac.uk/id/eprint/98341

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