Approximation Bounds for Minimum Information Loss Microaggregation

  • Authors:

  • Michael Laszlo;Sumitra Mukherjee

  • Affiliations:

  • Nova Southeastern University, Fort Lauderdale;Nova Southeastern University, Fort Lauderdale

  • Venue:
  • IEEE Transactions on Knowledge and Data Engineering
  • Year:
  • 2009

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Abstract

The NP-hard microaggregation problem seeks a partition of data points into groups of minimum specified size k, so as to minimize the sum of the squared euclidean distances of every point to its group's centroid. One recent heuristic provides an {\rm O}(k^3) guarantee for this objective function and an {\rm O}(k^2) guarantee for a version of the problem that seeks to minimize the sum of the distances of the points to its group's centroid. This paper establishes approximation bounds for another microaggregation heuristic, providing better approximation guarantees of {\rm O}(k^2) for the squared distance measure and {\rm O}(k) for the distance measure.