The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2011) will be will be held in San Diego, California. The conference will include two refereed paper tracks: the Research track, and the Industry & Government track; this document includes the call for papers for these tracks below.
Research Track - Call For Papers

We invite high-quality papers reporting original research on all aspects of knowledge discovery and data mining. We especially encourage submissions that promote the advancement of KDD as a scientific and engineering discipline and submissions that bridge between different disciplines. Papers are rigorously evaluated based on potential impact, novelty, repeatability and presentation.

Areas of interest include, but are not limited to:

* data mining algorithms (supervised, semi-supervised and unsupervised)
* data mining foundations and theory
* dimensionality reduction and feature selection
* mining dynamic and evolving data
* mining graph data
* mining semi-structured data
* mining spatial and temporal data
* mining stream data
* mixed-initiative data mining and active learning
* outlier analysis and anomaly detection
* parallel and distributed data mining algorithms
* pattern mining and association analysis
* robust and highly scalable data mining algorithms
* similarity search in data mining
* statistical methods in data mining
* topic models and matrix methods in data mining
* transfer learning and mining with auxiliary data sources
* adversarial data mining algorithms
* biological and medical data mining
* data mining for computational advertising
* data mining in social sciences and on social networks
* mining environmental and scientific data
* mining sensor data
* mining user behavioral and feedback data
* mining the Web and text data
* multimedia data mining
* data mining for other novel applications
* data integration and indexing for data mining
* data visualization for data mining
* KDD methodology and process
* platforms and systems for KDD
* pre-processing and post-processing in data mining
* security and privacy issues in data mining
* user modeling in data mining

All submitted papers will be judged based on their technical merit, rigor, significance, originality, repeatability, relevance, and clarity. Papers submitted to KDD'11 should be original work, not previously published in a peer-reviewed conference or journal. Substantially similar versions of the paper submitted to KDD'11 should not be under review in another peer-reviewed conference or journal during the KDD’11 reviewing period.

Repeatability guideline: Repeatability is a cornerstone of any scientific and engineering endeavor. To promote a solid foundation upon which future KDD work can be built, authors should make every effort to make code available as open source, and to employ public datasets, or make novel datasets available to the community. If this is not possible, please include a justification to that effect. Comparison to credible baseline systems and statistical significance of experimental results are expected for all papers with empirical evaluations.