ICDM 2015 - IEEE International Conference on Data Mining

The IEEE International Conference on Data Mining (ICDM) has established itself as the world's premier research conference in data mining. We invite high-quality papers reporting original research on all aspects of data mining, including applications, algorithms, software, and systems. We especially encourage submissions that promote the advancement of data mining as a scientific and engineering discipline and submissions that bridge different disciplines with data mining, such as statistics, machine learning, pattern recognition, databases, data warehousing, visualization, knowledge-based systems, and high-performance computing. Papers are rigorously evaluated based on novelty, technical merit, significance, clarity, relevance, and repeatability. Papers submitted should be original work, not previously published or currently reviewed in a peer-reviewed conference or journal.
Topics of Interest
Topics of interest include, but are not limited to:
Foundations, algorithms, models, and theory of data mining
Machine learning and statistical methods for data mining
Mining text, semi-structured, spatio-temporal, streaming, graph, web, multimedia data
Data mining systems and platforms, their efficiency, scalability, and privacy
Data mining in modeling, visualization, personalization, and recommendation
Applications of data mining in all domains including social, web, bioinformatics, and finance
Submission Guidelines
Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format (see the IEEE Computer Society Press Proceedings Author Guidelines). All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press.
Repeatability
Every effort should be made to enable the code and the datasets (or their closest publicly available equivalents) and all relevant parameter specifications available to reviewers and readers so that the results can be independently replicated. To retain anonymity of submissions, any such additional material should be anonymized as well, and be published on an anonymous website (such as DropBox, ResearchBib).