IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-05014-0_39.html
   My bibliography  Save this book chapter

On a Data Mining Framework for the Identification of Frequent Pattern Trends

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Marina Resta

    (University of Genova, DIEC)

Abstract

The work discusses a data mining framework that combining Self Organizing Maps (SOM) and Graphs paradigms is able to offer insights on the clusters structure of the mapping. The basics of the method rely in the use of trained SOM to define graphs from best matching units. In particular, we discuss the application to best matching units of two graphs topologies, originating the SOM-based Minimum Spanning Tree (SOM-MST), and the SOM-based Planar Maximally Filtered Graph (SOM-PMFG), respectively. We show that, working with financial time-series data, it is possible to capture the clusters structure of market assets, and to use such information for market active tradings. The discussion of results obtained working with stocks from Milan Stock Exchange concludes.

Suggested Citation

  • Marina Resta, 2014. "On a Data Mining Framework for the Identification of Frequent Pattern Trends," Springer Books, in: Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, edition 127, pages 173-176, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-05014-0_39
    DOI: 10.1007/978-3-319-05014-0_39
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-319-05014-0_39. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.