Iterative Linear Algebraic Data Mining Techniques Workshop

Modern relational datasets have rich topology and network scientists seek topological understanding. Recent research efforts propose linear algebraic techniques to aide in classifying, ranking, and clustering data entities. For a large dataset, iterative methods are employed to approximate solutions of algebraic equations. These solutions are in turn used to make algorithmic decisions, allowing further analysis to focus on small subsets of the large dataset. Numerical analysis that is aimed at iterative solvers in this context is a blooming research area. This workshop focuses on numerical analysis issues associated with linear algebraic approaches to data and graph analysis.

List of topics:

  • Solvers and preconditioning in graphs
  • Matrix functions for graph analysis
  • Spectral partitioning
  • Ranking via linear solves
  • Low-rank approximation
  • Markov chains
  • Applications
  • Models
  • Impact of skewed degree distribution
  • Eigenvector properties
  • Analysis of randomized algorithms
  • Analysis of convergence tolerances and their relationship to data mining quality
  • Early stopping criteria
  • Tensor analysis

    Tutorial: Iterative Methods for Data Analysis
    Sunday March 20, 2016

    Iterative Methods for Data Analysis
    by Van Emden Henson
    Sunday 1pm tutorial, 3-4 hours (aimed at graduate-level research assistants in numerical analysis).

    Starting Monday March 21, 2016, possibly continuing on Tuesday March 22
    Times to be announced

    Parallel session on Numerical Analysis of Iterative Linear Algebraic Data Mining Techniques.
    Links to submitted abstracts self-identified as part of this thematic track will come here once available. Please submit abstract and select the 'workshop' radio button at the submission link. Length of presenations is yet to be announced.