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Visual Data Mining pp 103–122Cite as

Visual Mining of Association Rules

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4404))

Abstract

Association Rules are one of the most widespread data mining tools because they can be easily mined, even from very huge database, and they provide valuable information for many application fields such as marketing, credit scoring, business, etc. The counterpart is that a massive effort is required (due to the large number of rules usually mined) in order to make actionable the retained knowledge. In this framework vizualization tools become essential to have a deep insight into the association structures and interactive features have to be exploited for highlighting the most relevant and meaningful rules.

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Author information

Authors and Affiliations

  1. Dipartimento di Scienze Mediche Preventive, Università di Napoli Federico II, Via S. Pansini, 5, 80131, Napoli, Italy

    Dario Bruzzese

  2. Dipartimento di Studi sullo Sviluppo Economico, Università di Macerata, Piazza Oberdan, 3, 62100, Macerata, Italy

    Cristina Davino

Authors
  1. Dario Bruzzese
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  2. Cristina Davino
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Editor information

Simeon J. Simoff Michael H. Böhlen Arturas Mazeika

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© 2008 Springer-Verlag Berlin Heidelberg

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Bruzzese, D., Davino, C. (2008). Visual Mining of Association Rules. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds) Visual Data Mining. Lecture Notes in Computer Science, vol 4404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71080-6_8

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  • DOI: https://doi.org/10.1007/978-3-540-71080-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71079-0

  • Online ISBN: 978-3-540-71080-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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