The 5th International Workshop on Knowledge Discovery in Inductive Databases (KDID'06)
The 5th International Workshop on Knowledge Discovery in Inductive Databases (KDID'06) will be held on September 18, 2006 in conjunction with the 17th European Conference on Machine Learning and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases in Berlin, Germany.
About the Workshop
Inductive databases (IDBs) represent a database view on data mining and knowledge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and manipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns. In the IDB framework, patterns become "first-class citizens" and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.
The IDB framework is appealing as a general framework for data mining, because it employs declarative queries instead of ad-hoc procedural constructs. As declarative queries are often formulated using constraints, inductive querying is closely related to constraint-based data mining. The IDB framework is also appealing for data mining applications, as it supports the entire KDD process, i.e., nontrivial multi-step KDD scenarios, rather than just individual data mining operations.
The goal of the KDID workshop is to bring together database and data mining researchers interested in the areas of inductive databases, inductive queries, constraint-based data mining, and data mining query
Previous editions of the workshop are
KDID'05 held in Porto, Portugal,
KDID'04 held in Pisa, Italy,
KDID'03 held in Cavtat-Dubrovnik, Croatia, and
KDID'02 held in Helsinki, Finland.
Refereed post-workshop proceedings appear in: S. Džeroski and J. Struyf editors, Knowledge Discovery in Inductive Databases, 5th International Workshop, KDID 2006, Berlin, Germany, September 18, 2006, Revised Selected and Invited Papers, Lecture Notes in Computer Science series, volume 4747, Springer, 2007. [Available here] or online at [SpringerLink].
A workshop report appears in: S. Džeroski and J. Struyf, "5th International Workshop on Knowledge Discovery in Inductive Databases (KDID06): Workshop Report", SIGKDD Explorations - Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining 9 (1), pp. 56-58, June, 2007. [Available here].
Topics of Interest (Non-exhaustive List)
- Models and theories of inductive databases
- Query languages for data mining
- Constraint-based data mining
- Pattern languages and primitives for data mining
- Efficient search algorithms for constraint-based mining
- Declarative bias formalisms in machine learning
- Local patterns detection
- Condensed representations of data and patterns
- Database support, coupling and primitives for data mining
- Coupling of database and data mining systems
- Integration of database languages such as SQL and XML with data mining
- Specific and application-oriented inductive databases