Data mining of vector–item patterns using neighborhood histograms

  • Denton A
  • Wu J
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The representation of multiple continuous attributes as dimensions in a vector space has been among the most influential concepts in machine learning and data mining. We consider sets of related continuous attributes as vector data and search for patterns that relate a vector attribute to one or more items. The presence of an item set defines a subset of vectors that may or may not show unexpected density fluctuations. We test for fluctuations by studying density histograms. A vector–item pattern is considered significant if its density histogram significantly differs from what is expected for a random subset of transactions. Using two different density measures, we evaluate the algorithm on two real data sets and one that was artificially constructed from time series data.

Author-supplied keywords

  • gene expression analysis
  • pattern mining
  • pattern significance
  • significance of classification
  • time series subsequences
  • vector space representation

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  • Anne M. Denton

  • Jianfei Wu

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