Association Rule Visualization and Pruning through Response-Style Data Organization and Clustering View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Leandro A. F. Fernandes , Ana Cristina Bicharra García

ABSTRACT

Association rules are a very popular non-supervised data mining technique for extracting co-relation in large set of data transactions. Although the vast use, the analysis of mined rules may be intricate for non-experts, and the technique effectiveness is constrained by the data dimensionality. This paper presents a pre-processing approach that uses (1) dual scaling in order to present the mined rules with some semantic contextualization that assists interpretation, and (2) mean shift clustering to reduce data dimensionality. We tested our model with real data collected from accident reports in petroleum industry. More... »

PAGES

71-80

References to SciGraph publications

  • 1999. Rule Evaluation Measures: A Unifying View in INDUCTIVE LOGIC PROGRAMMING
  • 2004-06. A Framework for Evaluating Knowledge-Based Interestingness of Association Rules in FUZZY OPTIMIZATION AND DECISION MAKING
  • 1996-12. Gleaning in the field of dual scaling in PSYCHOMETRIKA
  • 2004. Image and Video Segmentation by Anisotropic Kernel Mean Shift in COMPUTER VISION - ECCV 2004
  • 2003-06-18. Visualizing Frequent Itemsets, Association Rules, and Sequential Patterns in Parallel Coordinates in COMPUTATIONAL SCIENCE AND ITS APPLICATIONS — ICCSA 2003
  • 2005. Visualizing Association Rules in a Framework for Visual Data Mining in FROM INTEGRATED PUBLICATION AND INFORMATION SYSTEMS TO INFORMATION AND KNOWLEDGE ENVIRONMENTS
  • 1993-12. On quantifying different types of categorical data in PSYCHOMETRIKA
  • 2008. Visual Mining of Association Rules in VISUAL DATA MINING
  • 2000-07. Constraint-Based Rule Mining in Large, Dense Databases in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2007-06. Using metarules to organize and group discovered association rules in DATA MINING AND KNOWLEDGE DISCOVERY
  • Book

    TITLE

    Advances in Artificial Intelligence – IBERAMIA 2012

    ISBN

    978-3-642-34653-8
    978-3-642-34654-5

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-34654-5_8

    DOI

    http://dx.doi.org/10.1007/978-3-642-34654-5_8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1006067640


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