LIS 2010 - KDD 2010 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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 2010 should be original work, not previously published in a peer-reviewed conference or journal. Substantially similar versions of the paper submitted to KDD 2010 should not be under review in another peer-reviewed conference or journal during the KDD 2010 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.
Important Dates
abstract due on: Feb 2, 2010
paper due on: Feb 5, 2010
acceptance notification: April 30, 2010