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
  Tagging: 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.

References