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KDD 2014: 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

KDD 2014 is a premier interdisciplinary conference that brings together researchers and practitioners from all aspects of data science, data mining, knowledge discovery, large-scale data analytics, and big data. This is a very large conference.
When 24 August 2014 04:35 AM to
27 August 2014 04:35 AM
Where New York NY USA
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Subject areas:

  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
    6. Public policy
    7. Education
  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 mining for social good
  7. Data streams
  8. Design of experiments and sample survey
  9. Dimensionality reduction
  10. Economy, markets
    1. Viral marketing
    2. Online advertising
  11. Feature selection
  12. Foundations
  13. Graph mining
  14. Information extraction
  15. Mining rich data types
    1. Temporal / time series
    2. Spatial
    3. Text
    4. Sequence
    5. Unstructured
  16. Nearest neighbors
  17. Other
  18. Probabilistic methods
  19. Recommender systems
    1. Collaborative filtering
    2. Content based methods
    3. Evaluation and metrics
    4. Cold-start
  20. Rule and pattern mining
  21. Sampling
  22. Security and privacy
    1. Anonymization
    2. Spam detection
    3. Intrusion detection
  23. Semi-supervised learning
    1. Learning with partial labels
    2. Anomaly/novelty detection
  24. Sentiment and opinion mining
  25. Social
    1. Social and information networks
    2. Community detection
    3. Link prediction
    4. Social media
  26. 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
  27. Transfer learning
  28. Unsupervised learning
    1. Clustering
    2. Topic, graphical and latent variable models
    3. Matrix/tensor factorization
    4. Visualization
    5. Exploratory analysis
  29. User modeling
  30. Web mining

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