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