1.

Record Nr.

UNISA996466097903316

Titolo

Constraint-Based Mining and Inductive Databases [[electronic resource] ] : European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers / / edited by Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila

Pubbl/distr/stampa

Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006

ISBN

3-540-31351-6

Edizione

[1st ed. 2006.]

Descrizione fisica

1 online resource (X, 404 p.)

Collana

Lecture Notes in Artificial Intelligence ; ; 3848

Disciplina

005.74

Soggetti

Artificial intelligence

Computers

Database management

Information storage and retrieval

Pattern recognition

Artificial Intelligence

Computation by Abstract Devices

Database Management

Information Storage and Retrieval

Pattern Recognition

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Bibliographic Level Mode of Issuance: Monograph

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

The Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery -- A Relational Query Primitive for Constraint-Based Pattern Mining -- To See the Wood for the Trees: Mining Frequent Tree Patterns -- A Survey on Condensed Representations for Frequent Sets -- Adaptive Strategies for Mining the Positive Border of Interesting Patterns: Application to Inclusion Dependencies in Databases -- Computation of Mining Queries: An Algebraic Approach -- Inductive Queries on Polynomial Equations -- Mining Constrained Graphs: The Case of Workflow Systems -- CrossMine: Efficient Classification Across Multiple Database Relations -- Remarks on the Industrial Application of



Inductive Database Technologies -- How to Quickly Find a Witness -- Relevancy in Constraint-Based Subgroup Discovery -- A Novel Incremental Approach to Association Rules Mining in Inductive Databases -- Employing Inductive Databases in Concrete Applications -- Contribution to Gene Expression Data Analysis by Means of Set Pattern Mining -- Boolean Formulas and Frequent Sets -- Generic Pattern Mining Via Data Mining Template Library -- Inductive Querying for Discovering Subgroups and Clusters.