Edit Event: ICDM 2008

Event description:

The IEEE International Conference on Data Mining series (ICDM) has established itself as the world's premier research conference in data mining, providing a leading 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. In addition, 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 will feature workshops, tutorials, panels, and the ICDM data mining contest. Paper Submissions High quality papers in all data mining areas are solicited. Original papers exploring new directions will receive especially careful consideration. Papers that have already been accepted or are currently under review for other conferences or journals will not be considered for ICDM 2008. A selected number of IEEE ICDM 2008 accepted papers will be invited for possible inclusion, in expanded and revised form, in the Knowledge and Information Systems journal published by Springer-Verlag. ICDM Best Paper Awards 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. Strong, foundational results will be considered for the best research paper award and application-oriented submissions will be considered for the best application paper award. Workshops and Tutorials ICDM 2008 will host short and long tutorials as well as workshops that focus on new research directions and initiatives. All accepted workshop papers will be included in a separate workshop proceedings published by the IEEE Computer Society Press. ICDM Data Mining Contest A call for organizing a data mining contest will be issued to challenge researchers and practitioners with a real practical data mining problem. ==Topic of Interest== ===Data mining foundations=== * Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, pattern discovery, and association analysis) * Models and algorithms for new, structured, data types, such as arising in chemistry, biology, environment, and other scientific domains * Developing a unifying theory of data mining * Mining sequences and sequential data * Mining spatial and temporal datasets * Mining textual and unstructured datasets * Distributed data mining * High performance implementations of data mining algorithms * Privacy- and anonymity-preserving data analysis ===Mining in emerging domains=== * Stream Data Mining * Mining moving object data, RFID data, and data from sensor networks * Ubiquitous knowledge discovery * Mining multi-agent data * Mining and link analysis in networked settings: web, social and computer networks, and online communities * Mining the semantic web * Data mining in electronic commerce, such as recommendation, sponsored * web search, advertising, and marketing tasks ===Methodological aspects and the KDD process=== * Data pre-processing, data reduction, feature selection, and feature transformation * Quality assessment, interestingness analysis, and post-processing * Statistical foundations for robust and scalable data mining * Handling imbalanced data * Automating the mining process and other process related issues * Dealing with cost sensitive data and loss models * Human-machine interaction and visual data mining * Integration of data warehousing, OLAP and data mining * Data mining query languages * Security and data integrity ===Integrated KDD applications, systems, and experiences=== * Bioinformatics, computational chemistry, ecoinformatics * Computational finance, online trading, and analysis of markets * Intrusion detection, fraud prevention, and surveillance * Healthcare, epidemic modeling, and clinical research * Customer relationship management * Telecommunications, network and systems management * Sustainable mobility and intelligent transportation systems [[Category:Database]] [[Category:Machine learning]] [[Category:Information retrieval]]

Summary:

This is a minor edit Watch this page

Cancel