Category theory for computing science
Category theory for computing science
Knowledge discovery in databases: an overview
AI Magazine
A database perspective on knowledge discovery
Communications of the ACM
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Fundamentals of Algebraic Specification I
Fundamentals of Algebraic Specification I
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
The 3W Model and Algebra for Unified Data Mining
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
The generalized MDL approach for summarization
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Most knowledge discovery processes are biased since some part of the knowledge structure must be given before extraction. We propose a framework that avoids this bias by supporting all major model structures e.g. clustering, sequences, etc., as well as specifications of data and DM (Data Mining) algorithms, in the same language. A unification operation is provided to match automatically the data to the relevant DM algorithms in order to extract models and their related structure. The MDL principle is used to evaluate and rank models. This evaluation is based on the covering relation that links the data to the models. The notion of schema, related to the category theory, is the key concept of our approach. Intuitively, a schema is an algebraic specification enhanced by the union of types, and the concepts of list and relation. An example based on network alarm mining illustrates the process.