Subject Areas: Research Track

When an author submits a paper, they will be asked to select one primary subject area, and up to 5 secondary subject areas from the sets of terms below. The terms have been grouped to provide a somewhat systematic overview of topics relevant to the KDD conference. For example, a paper about information extraction from Web pages using latent variables could select the combination primary = text, secondary = [Topic, graphical and latent variable models, Web mining]; a paper about outlier detection in protein sequences could select primary = Anomaly/novelty detection, secondary = [bioinformatics, sequence]; and so on.

For reference the list of subject areas that will appear to authors and reviewers in the CMT conference management system:

  1. Adaptive learning
    1. Active learning
    2. Adaptive experimentation
    3. Adaptive models
  2. Applications
    1. Mobile
    2. E-commerce
    3. Healthcare and medicine
    4. Science
    5. Finance
  3. Big Data
    1. Distributed computing — cloud, map-reduce, MPI, others
    2. Scalable methods
    3. Large scale optimization
    4. Novel statistical techniques for big data
  4. Bioinformatics
  5. Causal discovery
  6. Data streams
  7. Design of experiments and sample survey
  8. Dimensionality reduction
  9. Economy, markets
    1. Viral marketing
    2. Online advertising
  10. Feature selection
  11. Foundations
  12. Graph mining
  13. Information extraction
  14. Mining rich data types
    1. Temporal / time series
    2. Spatial
    3. Text
    4. Sequence
    5. Unstructured
  15. Nearest neighbors
  16. Other
  17. Probabilistic methods
  18. Recommender systems
    1. Collaborative filtering
    2. Content based methods
    3. Evaluation and metrics
    4. Cold-start
  19. Rule and pattern mining
  20. Sampling
  21. Security and privacy
    1. Anonymization
    2. Spam detection
    3. Intrusion detection
  22. Semi-supervised learning
    1. Learning with partial labels
    2. Anomaly/novelty detection
  23. Sentiment and opinion mining
  24. Social
    1. Social and information networks
    2. Community detection
    3. Link prediction
    4. Social media
  25. Supervised learning
    1. Classification
    2. Regression
    3. Learning to rank
    4. Multi-label
    5. Neural networks
    6. Boosting
    7. Decision trees
    8. Support vector machines
  26. Transfer learning
  27. Unsupervised learning
    1. Clustering
    2. Topic, graphical and latent variable models
    3. Matrix/tensor factorization
    4. Visualization
    5. Exploratory analysis
  28. User modeling
  29. Web mining

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