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Knowledge Discovery in Inductive Databases [electronic resource] : 5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers / edited by Saso Dzeroski, Jan Struyf.

Contributor(s): Dzeroski, Saso [editor.] | Struyf, Jan [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Information Systems and Applications, incl. Internet/Web, and HCI: 4747Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2007Edition: 1st ed. 2007.Description: X, 301 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540755494.Subject(s): Data structures (Computer science) | Information theory | Database management | Artificial intelligence | Data Structures and Information Theory | Database Management | Artificial IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 005.73 | 003.54 Online resources: Click here to access online
Contents:
Invited Talk -- Value, Cost, and Sharing: Open Issues in Constrained Clustering -- Contributed Papers -- Mining Bi-sets in Numerical Data -- Extending the Soft Constraint Based Mining Paradigm -- On Interactive Pattern Mining from Relational Databases -- Analysis of Time Series Data with Predictive Clustering Trees -- Integrating Decision Tree Learning into Inductive Databases -- Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets -- An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results -- Beam Search Induction and Similarity Constraints for Predictive Clustering Trees -- Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs -- Extracting Trees of Quantitative Serial Episodes -- IQL: A Proposal for an Inductive Query Language -- Mining Correct Properties in Incomplete Databases -- Efficient Mining Under Rich Constraints Derived from Various Datasets -- Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth -- Discussion Paper -- Towards a General Framework for Data Mining.
In: Springer Nature eBook
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Invited Talk -- Value, Cost, and Sharing: Open Issues in Constrained Clustering -- Contributed Papers -- Mining Bi-sets in Numerical Data -- Extending the Soft Constraint Based Mining Paradigm -- On Interactive Pattern Mining from Relational Databases -- Analysis of Time Series Data with Predictive Clustering Trees -- Integrating Decision Tree Learning into Inductive Databases -- Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets -- An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results -- Beam Search Induction and Similarity Constraints for Predictive Clustering Trees -- Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs -- Extracting Trees of Quantitative Serial Episodes -- IQL: A Proposal for an Inductive Query Language -- Mining Correct Properties in Incomplete Databases -- Efficient Mining Under Rich Constraints Derived from Various Datasets -- Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth -- Discussion Paper -- Towards a General Framework for Data Mining.

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