The 2005 IEEE International Conference on Data Mining (IEEE ICDM '05) 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 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 * 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 * 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
High quality papers in all data mining areas are solicited. Original papers exploring new directions will receive especially careful and supportive reviews. Papers that have already been accepted or are currently under review at other conferences or journals will not be considered for publication at ICDM '05. Paper submissions should be limited to a maximum of 8 pages in the IEEE 2-column format (see the IEEE Computer Society Press Proceedings Author Guidelines at http://www.computer.org/cspress/instruct.htm), and will be reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity. Please use the Submission Form on the ICDM '05 website to submit your paper. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. A selected number of IEEE ICDM '05 accepted papers will be invited for possible inclusion, in an expanded and revised form, in the Knowledge and Information Systems journal (http://www.cs.uvm.edu/~kais/) published by Springer-Verlag. IEEE ICDM Best Paper Awards will be conferred at the conference on the authors of (1) the best research paper and (2) the best application paper. Application- oriented submissions will be considered for the best application paper award.
All paper submissions will be handled electronically. Detailed instructions are provided on the conference home page at http://www.cacs.louisiana.edu/~icdm05