ICDM 2013
Event Rating
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13th IEEE International Conference on Data Mining
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| Event in series | ICDM |
| Dates | 2013-12-07 (iCal) - 2013-12-10 |
| Homepage: | icdm2013.rutgers.edu/ |
| Location | |
| Location: | US/TX/Dallas, US/TX, US |
| Important dates | |
| Submissions: | 2013/06/21 |
| Table of Contents | |
The IEEE International Conference on Data Mining series (ICDM) has established itself as the world's premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications.
ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference features workshops, tutorials, panels and, since 2007, the ICDM data mining contest.
Topics of Interest
Topics related to the design, analysis and implementation of data mining theory, systems and applications are of interest. These include, but are not limited to the following areas:
- Foundations of data mining
- Data mining and machine learning algorithms and methods in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis), and in new areas
- Mining text and semi-structured data, and mining temporal, spatial and multimedia data
- Mining data streams
- Mining spatio-temporal data
- Mining with data clouds and Big Data
- Link and graph mining
- Pattern recognition and trend analysis
- Collaborative filtering/personalization
- Data and knowledge representation for data mining
- Query languages and user interfaces for mining
- Complexity, efficiency, and scalability issues in data mining
- Data pre-processing, data reduction, feature selection and feature transformation
- Post-processing of data mining results
- Statistics and probability in large-scale data mining
- Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining
- Integration of data warehousing, OLAP and data mining
- Human-machine interaction and visual data mining
- High performance and parallel/distributed data mining
- Quality assessment and interestingness metrics of data mining results
- Visual Analytics
- Security, privacy and social impact of data mining
- Data mining applications in bioinformatics, electronic commerce, Web, intrusion detection, finance, marketing, healthcare, telecommunications and other fields