Dec 152012
 

SMK 2011v4

:: DESCRIPTION

SMK (SupMaxK , a family of anti-monotonic measures) aims at the efficient discovery of discriminative patterns from biological data with high density and high dimensionality (e.g. Gene Expression data, and SNP data), and especially for the discovery of those patterns with relatively low-support but high discriminative power (e.g. odds ratio, information gain, p-value etc), which complements existing discriminative pattern mining algorithms.

::DEVELOPER

Data mining for biomedical informatics at the UMN

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Matlab

:: DOWNLOAD

 SMK

:: MORE INFORMATION

Citation

Gang Fang, Gaurav Pandey, Wen Wang, Manish Gupta, Michael Steinbach and Vipin Kumar
Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data
IEEE Transaction on Knowledge and Data Engineering (TKDE).2012 (vol. 24 no. 2)pp. 279-294

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