Buch, Englisch, 301 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 492 g
Reihe: Information Systems and Applications, incl. Internet/Web, and HCI
5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers
Buch, Englisch, 301 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 492 g
Reihe: Information Systems and Applications, incl. Internet/Web, and HCI
ISBN: 978-3-540-75548-7
Verlag: Springer
This book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in association with ECML/PKDD. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.
