Discovering all associations in discrete data using frequent minimally infrequent attribute sets

  • Authors:
  • Elke Eisenschmidt;Utz-Uwe Haus

  • Affiliations:
  • Institute for Mathematical Optimization, Department of Mathematics, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany;Institute for Operations Research, Department of Mathematics, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland

  • Venue:
  • Discrete Applied Mathematics
  • Year:
  • 2012

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Abstract

Associating categories with measured or observed attributes is a central challenge for discrete mathematics in life sciences. We propose a new concept to formalize this question: Given a binary matrix of objects and attributes, determine all attribute sets characterizing object sets of cardinality t"1 that do not characterize any object set of size t"2t"1. We determine how many such attribute sets exist, give an output-sensitive quasi-polynomial time algorithm to determine them, and show that k-sum matrix decompositions known from matroid theory are compatible with the characterization.