Papers on all aspects of knowledge discovery and data mining are
welcome. Areas of interest include, but are not limited to:

      - Theory and Foundational Issues in KDD

        * Data and Knowledge Representation
        * Logic for/of Knowledge Discovery
        * Expanding the Autonomy of Machine Discovers
        * Human Factors 
        * Scientific Discovery 
        * New Theory, Philosophy, and Methodology 

      - KDD Algorithms and Methods 

        * Machine Learning Methods 
        * Statistical Methods 
        * Heuristic Search 
        * Inductive Logic Programming
        * Deduction, Induction and Abduction 
        * Discovery of Exceptions and Deviations
        * Multi-criteria Evaluation and Data Mining Metrics
        * Hybrid and Multi-agent Methods 
        * Evaluation of Complexity, Efficiency, and Scalability of Algorithms

      - Process-Centric KDD

        * Models and Framework of the Knowledge Discovery Process
        * Data and Dimensionality Reduction 
        * Preprocessing and Postprocessing 
        * Interestingness Checking of Data and Rules
        * Management and Refinement for the Discovered Knowledge
        * Decomposition of Large Data Sets
        * Discretisation of Continuous Data 
        * Data and Knowledge Visualization 
        * Role of Domain Knowledge and Reuse of Discovered Knowledge 
        * KDD Process and Human Interaction

      - Soft Computing for KDD

        * Information Granulation and Granular Computing 
        * Rough Sets in Data Mining
        * Neural Networks, Probabilistic Reasoning 
        * Noise Handling and Uncertainty Management 
        * Hybrid Symbolic/Connectionist KDD Systems

      - High Performance Data Mining and Applications

        * Multi-Database Mining 
        * Data Mining in Advanced Databases (OODB, Spatial DB, Multimedia DB)
        * Database Reverse Engineering 
        * Integration of Data Warehousing, OLAP and Data Mining
        * Combining Data Mining with Database Querying
        * Parallel and Distributed Data Mining 
        * Data Mining on the Internet 
        * Multi-agent, Multi-task KDD Systems
        * Data Mining from Unstructured and Multimedia Data
        * Unification of Data Mining with Intelligent Information Retrieval
        * Security and Privacy Issues 
        * Successful/Innovative KDD Applications in Science, Engineering,
          Medicine, Business, Education, Government, and Industry 

Both research and applications papers are solicited. All submitted papers will be reviewed on the basis of technical quality, relevance to KDD, originality, significance, and clarity. Accepted papers are expected to be published in the conference proceedings by Springer-Verlag in the Lecture Notes in Artificial Intelligence series.

Selected number of PAKDD-2000 accepted papers will be expanded and revised for inclusion in major Japanese and/or international journals. Candidates include "Knowledge and Information Systems: An International Journal" by Springer-Verlag (http://kais.mines.edu/~kais/). PAKDD Best Paper Award will be conferred on the authors of the best paper at the conference. The winner will be honored US$500 and free registration.