The IEEE International Conference on Data Mining (ICDM) has established itself as the world's premier research conference in data mining. The 13th ICDM conference (ICDM '13) provides a premier forum for the dissemination of innovative, practical development experiences as well as original research results in data mining, spanning applications, algorithms, software and systems. The conference 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 high quality and novel research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state of the art in data mining. As an important part of the conference, the workshops program will focus on new research challenges and initiatives, and the tutorials program will cover emerging data mining technologies and the latest developments in data mining.
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