Generalizing itemset mining in a constraint programming setting
(2010) Inductive Databases and Constraint-Based Data Mining — ISBN: [978-1-4419-7737-3], published
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- Abstract
- (en) This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge 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 - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-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.
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Citations
Besson, J., Boulicaut, J.-F., Guns, T., & Nijssen, S. (2010). Generalizing itemset mining in a constraint programming setting. In Saso Dzeroski, Bart Goethals, Pance Panov (ed.), Inductive Databases and Constraint-Based Data Mining. https://doi.org/10.1007/978-1-4419-7738-0_5