Top Data Mining Challenges / Research Problem Areas

KDD 2006 panel report did a panel report on grand challenges
in the field of data mining. The report was published in the ACM SIGKDD
Explorations Newsletter and can be accessed from here. The
report identifies following areas as prominent ones for active research and development. Makes a good
repository for those looking for an idea to expand upon for their dissertation 🙂

  • Will
    you cheat for me please, my dear computer:
    Text-mining and understanding system
    that can use the web to pass standard tests, e.g. SAT in World Literature-based
    discovery of drug X side effects History.
  • Nip
    in the bud:
    Fraud detection based on company financial statements. (Can
    we find another Enron before it collapses?)
  • Autonomous
    Automatic tagging and classification of 1 billion digital
    photos on the web.
  • Social
    Networking 2.0:
    Mining user behaviors in interactions with multimedia
    data and use the knowledge extracted in this process to anticipate future behaviors
    or to diagnose medical or psychological conditions of the users. This generally
    falls under the area of Crossing the semantic gap between multi-media data
    and semantics
  • Where
    do I belong?:
    Link mining Challenge (extracting graphs describing
    entities and relationships from unstructured data)
  • Lots
    of Traffic!:
    Estimating large dataset predictive model - from 833
    traffic sensors in the Chicago
    metropolitan region and the goal is identifying anomalous traffic patterns.
  • Gold in the Text: Entity extraction and autonomous text analysis from large
    scale unstructured text repository.
  • And of
    course the genetics side, mining the proteome (Large-scale
    databases analysis from sequencing projects, micro array studies, gene-function
    studies, protein-protein interactions, comparative genomics, structural
    biology, and open source journal articles)

Also, the other areas of research interest mentioned in the data
mining literature are  

  • Parallelization
    of data mining algorithms.
  • Designing
    and developing scalable algorithms to operate on massive data sets.
  • distributed
    data mining; multiple topologies (local data, distributed app and so on …)
  • Standardizing
    the languages, underlying protocols, and application level integration for
    data mining and predictive modeling.
  • Systems to promote preserving privacy and security in the data mining.
  • Visualization of large datasets; mapping their corresponding associations, hierarchies and underlying patterns.


What Are The Grand Challenges for Data Mining? KDD-2006 Panel Report


One thought on “Top Data Mining Challenges / Research Problem Areas

  1. I know that's not the best resolution, but you can at least see the ship (sort of). When you go left or right, the ship "rolls" to that side. When you go forward or backward, the ship "pitches" towards the direction. This is actually quite easy because the ship is technically in 3d. While the screenshot looks like a 2d game, it's actual 3d with the camera up in the air pointed straight down.

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