Knowledge Discovery in Inductive Databases 5th International Workshop, KDID 2006 Berlin, Germany, September 18, 2006 Revised Selected and Invited Papers /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Džeroski, Sašo (Editor), Struyf, Jan (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
• Series: Lecture Notes in Computer Science, 4747
• Subjects: Computer science.; Data structures (Computer science).; Database management.; Artificial intelligence.; Computer Science.; Data Structures, Cryptology and Information Theory.; Database Management.; Artificial Intelligence (incl. Robotics).

Table of 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.