CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules

@inproceedings{Gray1998CCAIIACC,
  title={CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules},
  author={Brett Gray and Maria E. Orlowska},
  booktitle={PAKDD},
  year={1998}
}
We investigate the problem of mining interesting association rules over a pair of categorical attributes at any level of data granularity. We do this by integrating the rule discovery process with a form of clustering. This allows associations between groups of ;items to be formed where the groping of items is based on maximising the “interestingness” of the associations discovered. Previous work on mining generalised associations assumes either a distance metric on the attribute values or a… CONTINUE READING

Topics from this paper.

Similar Papers

Citations

Publications citing this paper.
SHOWING 1-10 OF 38 CITATIONS

Aided analysis for quality function deployment with an Apriori-based data mining approach

  • Int. J. Computer Integrated Manufacturing
  • 2010
VIEW 3 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Mining significant association rules from uncertain data

  • Data Mining and Knowledge Discovery
  • 2015
VIEW 2 EXCERPTS
CITES BACKGROUND

Product portfolio identification with data mining based on multi-objective GA

  • J. Intelligent Manufacturing
  • 2010
VIEW 2 EXCERPTS
CITES BACKGROUND & METHODS

A method to characterize dataset based on objective rule evaluation indices

  • Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security
  • 2009