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Multidimensional Fuzzy Partitioning of Attribute Ranges for Mining Quantitative Data

Attila Gyenesei, Jukka Teuhola, Multidimensional Fuzzy Partitioning of Attribute Ranges for Mining Quantitative Data. International Journal of Intelligent Systems 19(11), 1111–1126, 2004.

Abstract:

The article suggests a partitioning algorithm for quantitative
attributes to support the discovery of frequent fuzzy patterns among
transactions containing such attributes. More precisely, we present a
heuristic, multivariate, top-down partitioning algorithm that divides
attribute ranges into such intervals that the discovered frequent sets
are also dense, and thus probably more interesting to the user. Our
approach is fuzzy, so that the derived intervals have fuzzy bounds, and
thereby also the derived frequent sets are fuzzy. The crisp (non-fuzzy)
case is obtained as a special case. We evaluate the goodness of the
partitioning method by measuring the average and absolute information
amounts of the obtained fuzzy frequent sets. For the mining task, any
fuzzy frequent itemset mining method can be used. Experiments show that
the algorithm is able to do multidimensional partitioning in a balanced
way, and the "interestingness" of the obtained frequent sets is quite
high, especially for correlated attributes.

BibTeX entry:

@ARTICLE{jGyTe04a,
  title = {Multidimensional Fuzzy Partitioning of Attribute Ranges for Mining Quantitative Data},
  author = {Gyenesei, Attila and Teuhola, Jukka},
  journal = {International Journal of Intelligent Systems},
  volume = {19},
  number = {11},
  pages = {1111–1126},
  year = {2004},
  keywords = {data mining, frequent pattern, quantitative attribute, fuzzy set, partitioning},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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