Soft Concept Hierarchies to Summarise Data Streams and Highlight Anomalous Changes

Trevor P Martin, Y Shen, A Majidian

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

Abstract

A hierarchical approach is natural when managing large volumes of information, from both static (database) and dynamic (datastream) sources. Hierarchies allow progressively finer division into more specific categories, but frequently the categories are fuzzy rather than crisp. In this paper, we use fuzzy formal concept analysis to extract soft hierarchies from data. The hierarchies are used to classify data and to monitor changes over time by means of a fuzzy confidence measure for association analysis. A (simulated) stream of terrorism incident data is used as proof of concept.
Translated title of the contributionSoft Concept Hierarchies to Summarise Data Streams and Highlight Anomalous Changes
Original languageEnglish
Title of host publicationInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications
Subtitle of host publication13th International Conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010. Proceedings, Part II
EditorsEyke Hüllermeier, Rudolf Kruse, Frank Hoffmann
PublisherSpringer
Pages44-54
Number of pages11
ISBN (Electronic)9783642140587
ISBN (Print)9783642140570
DOIs
Publication statusPublished - 29 Jun 2010

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume81
ISSN (Print)1865-0929

Keywords

  • fuzzy formal concept hierarchies
  • fuzzy association rules
  • fuzzy confidence
  • dynamic data streams

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