Background and Related Work

  • Robert J. Hilderman
  • Howard J. Hamilton
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 638)

Abstract

In KDD, the knowledge that we seek to discover describes patterns in the data as opposed to knowledge about the data itself. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, sequential patterns, time series, contingency tables, summaries obtained using some hierarchical or taxonomic structure, and others. Typically, the number of patterns generated is very large, but only a few of these patterns are likely to be of any interest to the domain expert analyzing the data. The reason for this is that many of the patterns are either irrelevant or obvious, and do not provide new knowledge [105]. To increase the utility, relevance, and usefulness of the discovered patterns, techniques are required to reduce the number of patterns that need to be considered. Techniques which satisfy this goal are broadly referred to as interestingness measures.

Keywords

Association Rule General Impression Frequent Itemset Classification Rule Data Mining Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Robert J. Hilderman
    • 1
  • Howard J. Hamilton
    • 1
  1. 1.University of ReginaCanada

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