Induction and deduction are two opposite operations in data mining applications. Induction extracts knowledge in the form of, say, rules or decision trees from existing data, and deduction applies induction results to interpret new data.
An intelligent learning database (ILDB) system couples machine learning techniques with database and knowledge base technology. It starts with existing database technology and performs both induction and deduction. The integration of database technology, induction (from machine learning), and deduction (from knowledge-based systems) plays a key role in the construction of ILDB systems, as does the design of efficient induction and deduction algorithms. This talk will present a system structure for ILDB systems, and discuss the major research trends and problems for ILDB applications, such as noise handling and incremental induction. Reference First International Conference on the Practical Application of Knowledge Discovery and Data Mining, in London, on 24 April.