An introduction to database systems: vol. I (4th ed.)
An introduction to database systems: vol. I (4th ed.)
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A polynomial time computable metric between point sets
Acta Informatica
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Data Mining with SQL Server 2005
Data Mining with SQL Server 2005
Inductive logic programming for gene regulation prediction
Machine Learning
An inductive database and query language in the relational model
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Mining Views: Database Views for Data Mining
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Inductive databases in the relational model: the data as the bridge
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
A relational view of pattern discovery
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
An inductive database system based on virtual mining views
Data Mining and Knowledge Discovery
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In this demonstration, we will present the concepts and an implementation of an inductive database--- as proposed by Imielinski and Mannila --- in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.