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Dry Oxidation and Vacuum Annealing Treatments for Tuning the Wetting Properties of Carbon Nanotube Arrays
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Annealing and the Normalized N-Cut.

Tomáš Gedeon1, Albert E Parker, Collette Campion

  • 1Department of Mathematical Sciences, Montana State University, Bozeman, MT 59715, USA.

Pattern Recognition
|February 5, 2009
PubMed
Summary
This summary is machine-generated.

We present an annealing method for normalized N-cut graph computation. This technique efficiently segments data into K clusters, adaptable to various dataset sizes by controlling annealing parameters.

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Area of Science:

  • Graph theory
  • Computational mathematics
  • Data science

Background:

  • Normalized N-cut is a graph partitioning technique.
  • Existing methods may lack flexibility in determining the number of clusters (K).
  • High-dimensional data clustering and image segmentation are challenging problems.

Purpose of the Study:

  • To introduce a novel annealing procedure for computing the normalized N-cut of a weighted graph.
  • To demonstrate the algorithm's capability to yield solutions for the normalized 2-cut and N-cut problems.
  • To show the flexibility of the method in clustering data into K clusters, where 2 <= K <= N.

Main Methods:

  • An annealing procedure is described for graph partitioning.
  • The method involves phase transitions to approximate normalized 2-cut and then compute normalized N-cut.
  • Intermediate solutions offer refinements for splitting data into K clusters.

Main Results:

  • The annealing procedure successfully computes the normalized N-cut.
  • The method provides a sequence of cluster refinements from 2 to N clusters.
  • The algorithm requires only the upper limit N for the number of expected clusters.

Conclusions:

  • The proposed annealing approach offers a flexible and effective method for normalized N-cut graph partitioning.
  • The algorithm is applicable to image segmentation and high-dimensional data clustering.
  • Controlling the annealing parameter allows for obtaining any desired number of clusters K within the specified range.